xref: /aosp_15_r20/external/XNNPACK/src/subgraph.c (revision 4bdc94577ba0e567308109d787f7fec7b531ce36)

// 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 <stdlib.h>

#include <fp16.h>

#include <xnnpack.h>
#include <xnnpack/allocator.h>
#include <xnnpack/log.h>
#include <xnnpack/math.h>
#include <xnnpack/params.h>
#include <xnnpack/subgraph.h>


#ifndef XNN_ENABLE_SPARSE
  #error "XNN_ENABLE_SPARSE not defined"
#endif

enum xnn_status xnn_create_subgraph(
    uint32_t external_value_ids,
    uint32_t flags,
    xnn_subgraph_t* subgraph_out)
{
  struct xnn_subgraph* subgraph = NULL;
  enum xnn_status status = xnn_status_uninitialized;

  if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
    xnn_log_error("failed to create subgraph: XNNPACK is not initialized");
    goto error;
  }

  status = xnn_status_out_of_memory;

  subgraph = xnn_allocate_zero_memory(sizeof(struct xnn_subgraph));
  if (subgraph == NULL) {
    xnn_log_error("failed to allocate %zu bytes for subgraph descriptor", sizeof(struct xnn_subgraph));
    goto error;
  }

  subgraph->external_value_ids = external_value_ids;

  subgraph->values = xnn_allocate_zero_memory(external_value_ids * sizeof(struct xnn_value));
  if (subgraph->values == NULL) {
    xnn_log_error("failed to allocate %zu bytes for subgraph values",
      (size_t) external_value_ids * sizeof(struct xnn_value));
    goto error;
  }
  for (size_t i = 0; i < external_value_ids; i++) {
    subgraph->values[i].id = i;
  }
  subgraph->num_values = external_value_ids;
  subgraph->num_reserved_values = external_value_ids;

  *subgraph_out = subgraph;
  return xnn_status_success;

error:
  xnn_delete_subgraph(subgraph);
  return status;
}


struct xnn_value* xnn_subgraph_new_internal_value(xnn_subgraph_t subgraph)
{
  struct xnn_value* values = subgraph->values;
  const size_t size = subgraph->num_values;
  const size_t capacity = subgraph->num_reserved_values;
  if (capacity < size + 1) {
    const size_t new_capacity = max(min(capacity * 2, capacity + 512), capacity + 64);
    assert(new_capacity >= size + 1);
    values = xnn_reallocate_memory(values, new_capacity * sizeof(struct xnn_value));
    if (values == NULL) {
      xnn_log_error("failed to allocate %zu bytes for subgraph values",
        capacity * sizeof(struct xnn_value));
      return values;
    }

    memset(values + size, 0, (new_capacity - size) * sizeof(struct xnn_value));
    subgraph->num_reserved_values = new_capacity;
    subgraph->values = values;
  }
  subgraph->num_values = size + 1;
  struct xnn_value* new_value = values + size;
  new_value->id = size;
  return new_value;
}

void xnn_node_clear(struct xnn_node* node) {
  assert(node != NULL);
  memset(node, 0, sizeof(struct xnn_node));
}

void xnn_value_clear(struct xnn_value* value) {
  assert(value != NULL);
  memset(value, 0, sizeof(struct xnn_value));
}

void xnn_value_copy(
  struct xnn_value* dst_value,
  const struct xnn_value* src_value)
{
  // Note: Value ID stays unchanged

  dst_value->type = src_value->type;
  dst_value->datatype = src_value->datatype;
  dst_value->quantization = src_value->quantization;
  dst_value->shape = src_value->shape;
  dst_value->flags = src_value->flags;
  dst_value->data = src_value->data;
  dst_value->producer = src_value->producer;
  dst_value->first_consumer = src_value->first_consumer;
}

struct xnn_node* xnn_subgraph_new_node(xnn_subgraph_t subgraph)
{
  struct xnn_node* nodes = subgraph->nodes;
  const size_t size = subgraph->num_nodes;
  const size_t capacity = subgraph->num_reserved_nodes;

  if (capacity < size + 1) {
    const size_t new_capacity = max(min(capacity * 2, capacity + 512), capacity + 64);
    assert(new_capacity >= size + 1);
    nodes = xnn_reallocate_memory(nodes, new_capacity * sizeof(struct xnn_node));
    if (nodes == NULL) {
      xnn_log_error("failed to allocate %zu bytes for subgraph nodes",
        capacity * sizeof(struct xnn_node));
      return nodes;
    }

    memset(nodes + size, 0, (new_capacity - size) * sizeof(struct xnn_node));
    subgraph->num_reserved_nodes = new_capacity;
    subgraph->nodes = nodes;
  }
  subgraph->num_nodes = size + 1;
  struct xnn_node* new_node = nodes + size;
  new_node->id = size;
  return new_node;
}

void xnn_subgraph_add_nodes(xnn_subgraph_t subgraph, size_t num_nodes)
{
  struct xnn_node* nodes = subgraph->nodes;
  const size_t size = subgraph->num_nodes;
  const size_t capacity = subgraph->num_reserved_nodes;

  if (capacity < size + num_nodes) {
    const size_t new_capacity = max(min(capacity * 2, capacity + 512), capacity + max(num_nodes, 64));
    assert(new_capacity >= size + num_nodes);
    nodes = xnn_reallocate_memory(nodes, new_capacity * sizeof(struct xnn_node));
    if (nodes == NULL) {
      xnn_log_error("failed to allocate %zu bytes for subgraph nodes",
        capacity * sizeof(struct xnn_node));
      return;
    }

    memset(nodes + size, 0, (new_capacity - size) * sizeof(struct xnn_node));
    subgraph->num_reserved_nodes = new_capacity;
    subgraph->nodes = nodes;
  }
  subgraph->num_nodes = size + num_nodes;
  struct xnn_node* new_nodes = nodes + size;
  for (size_t i = 0; i < num_nodes; i++) {
    new_nodes[i].id = size + i;
  }
}

void xnn_subgraph_analyze_consumers_and_producers(xnn_subgraph_t subgraph)
{
  // Initialize producer/consumer fields to safe defaults.
  for (uint32_t i = 0; i < subgraph->num_values; i++) {
    struct xnn_value* value = &subgraph->values[i];
    value->producer = XNN_INVALID_NODE_ID;
    value->first_consumer = XNN_INVALID_NODE_ID;
    value->num_consumers = 0;
  }

  // Analyse Nodes' inputs and output and update Values' producer/consumer fields
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];

    for (uint32_t i = 0; i < node->num_inputs; i++) {
      const uint32_t input_id = node->inputs[i];
      assert(input_id < subgraph->num_values);

      if (subgraph->values[input_id].num_consumers++ == 0) {
        assert(subgraph->values[input_id].first_consumer == XNN_INVALID_NODE_ID);
        subgraph->values[input_id].first_consumer = n;
      }
    }

    for (uint32_t o = 0; o < node->num_outputs; o++) {
      const uint32_t output_id = node->outputs[o];
      assert(output_id < subgraph->num_values);

      assert(subgraph->values[output_id].producer == XNN_INVALID_NODE_ID);
      subgraph->values[output_id].producer = n;
    }
  }

  // Count extra consumer for Values which are external outputs.
  // Remove unreferenced values.
  for (uint32_t i = 0; i < subgraph->num_values; i++) {
    struct xnn_value* value = &subgraph->values[i];
    if (xnn_value_is_external_output(value)) {
      value->num_consumers += 1;
    }
  }
}

#define XNN_LAYOUT_FLAG_COMPATIBLE_NCHW      1
#define XNN_LAYOUT_FLAG_COMPATIBLE_NHWC2NCHW 2
#define XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC 4
#define XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER 8

uint32_t xnn_check_nchw_compatibility(xnn_subgraph_t subgraph, struct xnn_node* node) {
  if (node->compute_type != xnn_compute_type_fp32) {
    if (node->type != xnn_node_type_invalid) {
      xnn_log_info(
          "Node %s compute type %d is incompatible with sparse inference",
          xnn_node_type_to_string(node->type), node->compute_type);
    }
    return 0;
  }

  switch (node->type) {
    case xnn_node_type_convolution_2d:
      // Supported cases:
      // - 1x1 convolution (no stride, no dilation, no padding, no groups)
      // - 3x3 stride-2 convolution (no dilation, padding 1 on each side, no groups, 3 input channels)
      if (node->params.convolution_2d.groups != 1) {
        xnn_log_info("Node %s groups (%" PRIu32 ") "
                     "is incompatible with sparse inference",
                     xnn_node_type_to_string(node->type),
                     node->params.convolution_2d.groups);
        return 0;
      }
      if ((node->params.convolution_2d.dilation_height | node->params.convolution_2d.dilation_width) != 1) {
        xnn_log_info("Node %s dilation (height=%" PRIu32 ", width=%" PRIu32 ") "
                     "is incompatible with sparse inference",
                     xnn_node_type_to_string(node->type),
                     node->params.convolution_2d.dilation_height,
                     node->params.convolution_2d.dilation_width);
        return 0;
      }
      if ((node->params.convolution_2d.kernel_height | node->params.convolution_2d.kernel_width) == 1) {
        if ((node->params.convolution_2d.input_padding_top | node->params.convolution_2d.input_padding_right |
             node->params.convolution_2d.input_padding_bottom | node->params.convolution_2d.input_padding_left) != 0) {
          xnn_log_info("Node %s (1x1 kernel) padding (top=%" PRIu32 ", right=%" PRIu32", bottom=%" PRIu32 ", left=%" PRIu32") "
                       "is incompatible with sparse inference",
                       xnn_node_type_to_string(node->type),
                       node->params.convolution_2d.input_padding_top,
                       node->params.convolution_2d.input_padding_right,
                       node->params.convolution_2d.input_padding_bottom,
                       node->params.convolution_2d.input_padding_left);
          return 0;
        }
        if ((node->params.convolution_2d.subsampling_height | node->params.convolution_2d.subsampling_width) != 1) {
          xnn_log_info("Node %s (1x1 kernel) subsampling (height=%" PRIu32 ", width=%" PRIu32 ") "
                       "is incompatible with sparse inference",
                       xnn_node_type_to_string(node->type),
                       node->params.convolution_2d.subsampling_height,
                       node->params.convolution_2d.subsampling_width);
          return 0;
        }
        return XNN_LAYOUT_FLAG_COMPATIBLE_NCHW;
      } else if (node->params.convolution_2d.kernel_height == 3 && node->params.convolution_2d.kernel_width == 3) {
        if (node->params.convolution_2d.input_padding_top != 1 || node->params.convolution_2d.input_padding_right != 1 ||
            node->params.convolution_2d.input_padding_bottom != 1 || node->params.convolution_2d.input_padding_left != 1) {
          xnn_log_info("Node %s (3x3 kernel) padding (top=%" PRIu32 ", right=%" PRIu32 ", bottom=%" PRIu32 ", left=%" PRIu32 ") "
                       "is incompatible with sparse inference",
                       xnn_node_type_to_string(node->type),
                       node->params.convolution_2d.input_padding_top,
                       node->params.convolution_2d.input_padding_right,
                       node->params.convolution_2d.input_padding_bottom,
                       node->params.convolution_2d.input_padding_left);
          return 0;
        }
        if ((node->params.convolution_2d.subsampling_height | node->params.convolution_2d.subsampling_width) != 2) {
          xnn_log_info("Node %s (3x3 kernel) subsampling (height=%" PRIu32 ", width=%" PRIu32 ") "
                       "is incompatible with sparse inference",
                       xnn_node_type_to_string(node->type),
                       node->params.convolution_2d.subsampling_height,
                       node->params.convolution_2d.subsampling_width);
          return 0;
        }
        if (node->params.convolution_2d.group_input_channels != 3) {
          xnn_log_info("Node %s (3x3 kernel) input channels (%zu) "
                       "is incompatible with sparse inference",
                       xnn_node_type_to_string(node->type),
                       node->params.convolution_2d.group_input_channels);
          return 0;
        }
        return XNN_LAYOUT_FLAG_COMPATIBLE_NHWC2NCHW;
      }
      return 0;
    case xnn_node_type_depthwise_convolution_2d:
      // Supported cases:
      // - 3x3 stride-1 convolution (no dilation, padding 1 on each side)
      // - 3x3 stride-2 convolution (no dilation, padding 1 on each side)
      // - 5x5 stride-1 convolution (no dilation, padding 2 on each side)
      // - 5x5 stride-2 convolution (no dilation, padding 2 on each side)
      if ((node->params.depthwise_convolution_2d.dilation_height | node->params.depthwise_convolution_2d.dilation_width) != 1) {
        xnn_log_info("Node %s dilation (height=%" PRIu32 ", width=%" PRIu32 ") "
                     "is incompatible with sparse inference",
                     xnn_node_type_to_string(node->type),
                     node->params.convolution_2d.dilation_height,
                     node->params.convolution_2d.dilation_width);
        return 0;
      }
      if (node->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING) {
        xnn_log_info("Node %s flags (%" PRIu32 ") has padding incompatible with sparse inference",
                     xnn_node_type_to_string(node->type),
                     node->flags);
        return 0;
      }
      if (node->params.depthwise_convolution_2d.depth_multiplier != 1) {
        xnn_log_info("Node %s depth_multiplier (%" PRIu32 ") is incompatible with sparse inference",
                     xnn_node_type_to_string(node->type),
                     node->params.depthwise_convolution_2d.depth_multiplier);
        return 0;
      }
      if (node->params.depthwise_convolution_2d.subsampling_height != node->params.depthwise_convolution_2d.subsampling_width) {
        xnn_log_info("Node %s subsampling (height=%" PRIu32 ", width=%" PRIu32 ") "
                     "is incompatible with sparse inference",
                     xnn_node_type_to_string(node->type),
                     node->params.depthwise_convolution_2d.subsampling_height,
                     node->params.depthwise_convolution_2d.subsampling_width);
        return 0;
      }
      switch (node->params.depthwise_convolution_2d.subsampling_height) {
        case 1:
        case 2:
          break;
        default:
          xnn_log_info("Node %s subsampling_height (%" PRIu32 ") "
                       "is incompatible with sparse inference",
                        xnn_node_type_to_string(node->type),
                        node->params.depthwise_convolution_2d.subsampling_height);
          return 0;
      }
      if (node->params.depthwise_convolution_2d.kernel_height != node->params.depthwise_convolution_2d.kernel_width) {
         xnn_log_info("Node %s kernel (height=%" PRIu32 ", width=%" PRIu32 ") "
                      "is incompatible with sparse inference",
                      xnn_node_type_to_string(node->type),
                      node->params.depthwise_convolution_2d.kernel_height,
                      node->params.depthwise_convolution_2d.kernel_width);
        return 0;
      }
      switch (node->params.depthwise_convolution_2d.kernel_height) {
        case 3:
          if (node->params.depthwise_convolution_2d.input_padding_top == 1 &&
              node->params.depthwise_convolution_2d.input_padding_right == 1 &&
              node->params.depthwise_convolution_2d.input_padding_bottom == 1 &&
              node->params.depthwise_convolution_2d.input_padding_left == 1) {
            return XNN_LAYOUT_FLAG_COMPATIBLE_NCHW;
          } else {
            xnn_log_info("Node %s (3x3 kernel) padding "
                         "(top=%" PRIu32 ", right=%" PRIu32 ", bottom=%" PRIu32 ", left=%" PRIu32 ") "
                         "is incompatible with sparse inference",
                         xnn_node_type_to_string(node->type),
                         node->params.depthwise_convolution_2d.input_padding_top,
                         node->params.depthwise_convolution_2d.input_padding_right,
                         node->params.depthwise_convolution_2d.input_padding_bottom,
                         node->params.depthwise_convolution_2d.input_padding_left);
            return 0;
          }
        case 5:
          if (node->params.depthwise_convolution_2d.input_padding_top == 2 &&
              node->params.depthwise_convolution_2d.input_padding_right == 2 &&
              node->params.depthwise_convolution_2d.input_padding_bottom == 2 &&
              node->params.depthwise_convolution_2d.input_padding_left == 2) {
            return XNN_LAYOUT_FLAG_COMPATIBLE_NCHW;
          } else {
            xnn_log_info("Node %s (5x5 kernel) padding "
                         "(top=%" PRIu32 ", right=%" PRIu32 ", bottom=%" PRIu32 ", left=%" PRIu32 ") "
                         "is incompatible with sparse inference",
                         xnn_node_type_to_string(node->type),
                         node->params.depthwise_convolution_2d.input_padding_top,
                         node->params.depthwise_convolution_2d.input_padding_right,
                         node->params.depthwise_convolution_2d.input_padding_bottom,
                         node->params.depthwise_convolution_2d.input_padding_left);
            return 0;
          }
        default:
          return 0;
      }
    case xnn_node_type_depth_to_space:
      return XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC;
    case xnn_node_type_global_average_pooling_2d:
      return XNN_LAYOUT_FLAG_COMPATIBLE_NCHW | XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC;
    case xnn_node_type_add2:
    case xnn_node_type_multiply2:
      assert(node->num_inputs == 2);
      assert(node->num_outputs == 1);
      if (subgraph->values[node->inputs[0]].shape.num_dims != 4 ||
          subgraph->values[node->inputs[1]].shape.num_dims != 4)
      {
        xnn_log_info("Node %s inputs shape is incompatible with sparse inference",
                     xnn_node_type_to_string(node->type));
        return 0;
      }

      if (subgraph->values[node->inputs[0]].data != NULL) {
        // Check that the first input is representable as either a scalar, or a vector
        size_t num_nonunit_dims = 0;
        for (uint32_t i = 0; i < subgraph->values[node->inputs[0]].shape.num_dims; i++) {
          if (subgraph->values[node->inputs[0]].shape.dim[i] != 1) {
            num_nonunit_dims += 1;
          }
        }
        if (num_nonunit_dims > 1) {
          return 0;
        }
      }

      if (subgraph->values[node->inputs[1]].data != NULL) {
        // Check that the second input is representable as either a scalar, or a vector
        size_t num_nonunit_dims = 0;
        for (uint32_t i = 0; i < subgraph->values[node->inputs[0]].shape.num_dims; i++) {
          if (subgraph->values[node->inputs[0]].shape.dim[i] != 1) {
            num_nonunit_dims += 1;
          }
        }
        if (num_nonunit_dims > 1) {
          return 0;
        }
      }

      return XNN_LAYOUT_FLAG_COMPATIBLE_NCHW;
    case xnn_node_type_static_resize_bilinear_2d:
      if (subgraph->values[node->inputs[0]].shape.dim[1] > 1 &&
          subgraph->values[node->inputs[0]].shape.dim[2] > 1) {
        return XNN_LAYOUT_FLAG_COMPATIBLE_NCHW;
      } else {
        xnn_log_info("Node %s inputs shape is incompatible with sparse inference",
                     xnn_node_type_to_string(node->type));
        return 0;
      }
    case xnn_node_type_abs:
    case xnn_node_type_bankers_rounding:
    case xnn_node_type_ceiling:
    case xnn_node_type_clamp:
    case xnn_node_type_elu:
    case xnn_node_type_floor:
    case xnn_node_type_hardswish:
    case xnn_node_type_leaky_relu:
    case xnn_node_type_negate:
    case xnn_node_type_sigmoid:
    case xnn_node_type_square:
      assert(node->num_inputs == 1);
      assert(node->num_outputs == 1);
      if (subgraph->values[node->inputs[0]].shape.num_dims == 4) {
        return XNN_LAYOUT_FLAG_COMPATIBLE_NCHW;
      } else {
        xnn_log_info("Node %s inputs shape is incompatible with sparse inference",
                     xnn_node_type_to_string(node->type));
        return 0;
      }
    default:
      return false;
  }
}

void xnn_subgraph_rewrite_for_nchw(xnn_subgraph_t subgraph)
{
  // Convert parts of the subgraph to NCHW for sparse inference
  // Step 1: detect NCHW-compatible Nodes
  // Step 2: detect NCHW-compatible clusters (run connected components graph algorithm)
  // Step 3: check that all NCHW-compatible Values are consumed only by NCHW-compatible Nodes
  // Step 4: switch Values' layout to NCHW
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    node->layout_flags = xnn_check_nchw_compatibility(subgraph, node);
    xnn_log_debug("Node #%" PRIu32 ": %s (NCHW: %s, NHWC->NCHW: %s, NCHW->NHWC: %s)",
      n, xnn_node_type_to_string(node->type),
      node->layout_flags & XNN_LAYOUT_FLAG_COMPATIBLE_NCHW ? "yes" : "no",
      node->layout_flags & XNN_LAYOUT_FLAG_COMPATIBLE_NHWC2NCHW ? "yes" : "no",
      node->layout_flags & XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC ? "yes" : "no");
  }

  // Run Shiloach-Vishkin connected components algorithm i.e. find all
  // XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC nodes and set them as cluster leaders
  // to all the producer nodes
  bool update = false;
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    node->cluster_leader = n;
    if (node->layout_flags & XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC) {
      for (uint32_t i = 0; i < node->num_inputs; i++) {
        const struct xnn_value* value = &subgraph->values[node->inputs[i]];
        if (value->data != NULL) {
          // Static data, skip this input value. Compatibility of this static input with NCHW layout was validated
          // during the initial NCHW compatibility check for the Node.
          continue;
        }
        if (xnn_value_is_external(value)) {
          // External value, invalid cluster
          node->layout_flags |= XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER;
          continue;
        }
        const uint32_t producer_id = value->producer;
        assert(producer_id != XNN_INVALID_NODE_ID);
        assert(producer_id < n);
        struct xnn_node* producer_node = &subgraph->nodes[producer_id];
        if ((producer_node->layout_flags & (XNN_LAYOUT_FLAG_COMPATIBLE_NHWC2NCHW | XNN_LAYOUT_FLAG_COMPATIBLE_NCHW)) != 0 &&
            (producer_node->layout_flags & XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER) == 0)
        {
          producer_node->layout_flags &= ~XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC;
          if (producer_node->cluster_leader != node->cluster_leader) {
            producer_node->cluster_leader = node->cluster_leader = math_max_u32(producer_node->cluster_leader, node->cluster_leader);
            update = true;
          }
        } else {
          node->layout_flags |= XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER;
        }
      }
    }
  }
  // No NCHW2NHWC compatible nodes have been found thus the graph rewriting
  // practically cannot happen.
  if (!update) {
    return;
  }
  // Propagate the cluster leader to other nodes in the graph untill all the
  // nodes in the cluster is not updated
  while (update) {
    update = false;
    for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
      struct xnn_node* node = &subgraph->nodes[n];
      if (node->layout_flags & XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER) {
        continue;
      }

      if ((node->layout_flags & (XNN_LAYOUT_FLAG_COMPATIBLE_NCHW | XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC)) == 0) {
        continue;
      }

      for (uint32_t i = 0; i < node->num_inputs; i++) {
        const struct xnn_value* value = &subgraph->values[node->inputs[i]];
        if (value->data != NULL) {
          // Static data, skip this input value. Compatibility of this static input with NCHW layout was validated
          // during the initial NCHW compatibility check for the Node.
          continue;
        }
        if (xnn_value_is_external(value)) {
          // External value, invalid cluster
          node->layout_flags |= XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER;
          continue;
        }
        const uint32_t producer_id = value->producer;
        assert(producer_id != XNN_INVALID_NODE_ID);
        assert(producer_id < n);
        struct xnn_node* producer_node = &subgraph->nodes[producer_id];
        if ((producer_node->layout_flags & (XNN_LAYOUT_FLAG_COMPATIBLE_NHWC2NCHW | XNN_LAYOUT_FLAG_COMPATIBLE_NCHW)) != 0 &&
            (producer_node->layout_flags & XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER) == 0)
        {
          producer_node->layout_flags &= ~XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC;
          if (producer_node->cluster_leader != node->cluster_leader) {
            producer_node->cluster_leader = node->cluster_leader = math_max_u32(producer_node->cluster_leader, node->cluster_leader);
            update = true;
          }
        } else {
          node->layout_flags |= XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER;
        }
      }
    }
  }
  // Propagate XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER flags up to the cluster leaders
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    subgraph->nodes[node->cluster_leader].layout_flags |= node->layout_flags & XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER;
  }
  // Check that all Values consumed by NCHW-compatible cluster don't have NCHW-incompatible consumers
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    if ((subgraph->nodes[node->cluster_leader].layout_flags & XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER) != 0) {
      continue;
    }

    if ((node->layout_flags & (XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC | XNN_LAYOUT_FLAG_COMPATIBLE_NCHW)) == 0) {
      continue;
    }

    for (uint32_t i = 0; i < node->num_inputs; i++) {
      struct xnn_value* value = &subgraph->values[node->inputs[i]];
      if (value->data != NULL) {
        // Static data, skip this input value because it doesn't have a producer Node.
        continue;
      }
      assert(!xnn_value_is_external(value));
      value->num_nchw_compatible_consumers += 1;
    }
  }
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    if ((subgraph->nodes[node->cluster_leader].layout_flags & XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER) != 0) {
      continue;
    }

    if ((node->layout_flags & (XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC | XNN_LAYOUT_FLAG_COMPATIBLE_NCHW)) == 0) {
      continue;
    }

    for (uint32_t i = 0; i < node->num_inputs; i++) {
      const struct xnn_value* value = &subgraph->values[node->inputs[i]];
      if (value->data != NULL) {
        // Static data, skip this input value because it doesn't have a producer Node.
        continue;
      }
      assert(!xnn_value_is_external(value));
      assert(value->num_nchw_compatible_consumers > 0);
      if (value->num_nchw_compatible_consumers != value->num_consumers) {
        subgraph->nodes[node->cluster_leader].layout_flags |= XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER;
      }
    }
  }
  // Evaluate if it is profitable to run the model as sparse:
  // - Compute the number of parameters and zeroes in 1x1 Convolution weights
  // - Disable sparse rewriting for clusters without 1x1 Convolutions (num_params == 0)
  //   or with less than 2/3rd of zeroes in 1x1 Convolution filters
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    if ((subgraph->nodes[node->cluster_leader].layout_flags & XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER) != 0) {
      continue;
    }

    if (node->type == xnn_node_type_convolution_2d &&
        max(node->params.convolution_2d.kernel_height, node->params.convolution_2d.kernel_width) == 1)
    {
      assert(node->num_inputs >= 2);

      const struct xnn_value* filter = &subgraph->values[node->inputs[1]];
      assert(filter->data != NULL);
      assert(filter->shape.num_dims == 4);

      const size_t num_params = filter->shape.dim[0] * filter->shape.dim[3];
      subgraph->nodes[node->cluster_leader].num_params += num_params;

      const float* data = (const float*) filter->data;
      size_t num_zeroes = 0;
      for (size_t i = 0; i < num_params; i++) {
        num_zeroes += (size_t) (data[i] == 0.0f);
      }
      xnn_log_debug("1x1 Convolution 2D Node #%" PRIu32 ": %zu / %zu sparsity", n, num_zeroes, num_params);
      subgraph->nodes[node->cluster_leader].num_zeroes += num_zeroes;
    }
  }
  bool use_nchw_layout = false;
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    if ((subgraph->nodes[node->cluster_leader].layout_flags & XNN_LAYOUT_FLAG_INCOMPATIBLE_CLUSTER) != 0) {
      continue;
    }

    if ((node->layout_flags & (XNN_LAYOUT_FLAG_COMPATIBLE_NCHW2NHWC | XNN_LAYOUT_FLAG_COMPATIBLE_NCHW)) == 0) {
      continue;
    }

    if (subgraph->nodes[node->cluster_leader].num_zeroes * 3 <= subgraph->nodes[node->cluster_leader].num_params * 2) {
      xnn_log_info("Node #%" PRIu32 ": sparse inference disabled: 1x1 Convolutions contain %zu / %zu zero weights",
        n, subgraph->nodes[node->cluster_leader].num_zeroes, subgraph->nodes[node->cluster_leader].num_params);
      continue;
    }

    for (uint32_t i = 0; i < node->num_inputs; i++) {
      struct xnn_value* value = &subgraph->values[node->inputs[i]];
      if (value->data != NULL) {
        // Static data, skip this input value because it doesn't have a producer Node.
        continue;
      }
      assert(!xnn_value_is_external(value));
      assert(value->num_nchw_compatible_consumers > 0);
      assert(value->num_nchw_compatible_consumers == value->num_consumers);
      if (value->layout != xnn_layout_type_nchw) {
        value->layout = xnn_layout_type_nchw;
        xnn_log_info("set Value #%"PRIu32" layout to NCHW", node->inputs[i]);
        use_nchw_layout = true;
      }
    }
  }
  if (use_nchw_layout) {
    xnn_log_info("XNNPACK has switched to sparse inference mode!");
  }
}

bool xnn_subgraph_rewrite_for_fp16(xnn_subgraph_t subgraph)
{
  xnn_log_info("Analyzing subgraph for FP16 compatibility");

  // Convert tensors and operators in the subgraph to FP16
  // 1. Check that all operators in the subgraph are supported in FP16.
  // 2. Indicate values that must be converted to FP16.
  // 3. Replace FP32 Values with FP16 Values as Nodes' inputs/outputs.
  // 4. Insert FP32->FP16 Convert Nodes for external FP32 inputs and FP16->FP32 Convert Nodes for external outputs.

  // Check that all operators in the subgraph are supported in FP16, bail out on any unsupported one.
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    if (node->type == xnn_node_type_invalid) {
      // Node was fused away, skip.
      continue;
    }

    if (node->compute_type != xnn_compute_type_fp32) {
      xnn_log_warning("FP16 rewrite aborted: node #%" PRIu32 " (%s) is not FP32", n, xnn_node_type_to_string(node->type));
      return false;
    }
    for (uint32_t i = 0; i < node->num_inputs; i++) {
      if (subgraph->values[node->inputs[i]].layout == xnn_layout_type_nchw) {
        xnn_log_warning(
          "FP16 rewrite aborted: input #%" PRIu32 " (Value #%" PRIu32 ") of node #%" PRIu32 " (%s) has NCHW layout",
          i, node->inputs[i], n, xnn_node_type_to_string(node->type));
        return false;
      }
    }
    for (uint32_t o = 0; o < node->num_outputs; o++) {
      if (subgraph->values[node->outputs[o]].layout == xnn_layout_type_nchw) {
        xnn_log_warning(
          "FP16 rewrite aborted: output #%" PRIu32 " (Value #%" PRIu32 ") of node #%" PRIu32 " (%s) has NCHW layout",
          o, node->outputs[o], n, xnn_node_type_to_string(node->type));
        return false;
      }
    }
    switch (node->type) {
      case xnn_node_type_abs:
      case xnn_node_type_add2:
      case xnn_node_type_divide:
      case xnn_node_type_maximum2:
      case xnn_node_type_minimum2:
      case xnn_node_type_multiply2:
      case xnn_node_type_concatenate2:
      case xnn_node_type_concatenate3:
      case xnn_node_type_concatenate4:
      case xnn_node_type_squared_difference:
      case xnn_node_type_subtract:
        for (uint32_t i = 0; i < node->num_inputs; i++) {
          if (subgraph->values[node->inputs[i]].data != NULL) {
            xnn_log_warning("FP16 rewrite aborted: node #%" PRIu32 " (%s) has static input %" PRIu32,
              n, xnn_node_type_to_string(node->type), i);
            return false;
          }
        }
        break;
      case xnn_node_type_average_pooling_2d:
      case xnn_node_type_bankers_rounding:
      case xnn_node_type_ceiling:
      case xnn_node_type_clamp:
      case xnn_node_type_convolution_2d:
      case xnn_node_type_deconvolution_2d:
      case xnn_node_type_depthwise_convolution_2d:
      case xnn_node_type_depth_to_space:
      case xnn_node_type_elu:
      case xnn_node_type_even_split2:
      case xnn_node_type_even_split3:
      case xnn_node_type_even_split4:
      case xnn_node_type_floor:
      case xnn_node_type_fully_connected:
      case xnn_node_type_global_average_pooling_2d:
      case xnn_node_type_hardswish:
      case xnn_node_type_leaky_relu:
      case xnn_node_type_max_pooling_2d:
      case xnn_node_type_negate:
      case xnn_node_type_prelu:
      case xnn_node_type_sigmoid:
      case xnn_node_type_softmax:
      case xnn_node_type_static_constant_pad:
      case xnn_node_type_static_reshape:
      case xnn_node_type_static_resize_bilinear_2d:
      case xnn_node_type_static_transpose:
      case xnn_node_type_square:
      case xnn_node_type_square_root:
        break;
      default:
        xnn_log_warning("FP16 rewrite aborted: node #%" PRIu32 " (%s) is not supported for FP16 inference",
          n, xnn_node_type_to_string(node->type));
        return false;
    }
  }

  // Annotate Values to be converted to FP16 as FP16-compatible.
  // Note that static weights in [Depthwise] Convolution, Fully Connected, and PReLU Nodes remain FP32,
  // they will be converted to FP16 during weight repacking when the operator is created.
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    switch (node->type) {
      case xnn_node_type_convolution_2d:
      case xnn_node_type_deconvolution_2d:
      case xnn_node_type_depthwise_convolution_2d:
      case xnn_node_type_fully_connected:
      case xnn_node_type_prelu:
        subgraph->values[node->inputs[0]].fp16_compatible = true;
        subgraph->values[node->outputs[0]].fp16_compatible = true;
        break;
      default:
        for (uint32_t i = 0; i < node->num_inputs; i++) {
          subgraph->values[node->inputs[i]].fp16_compatible = true;
        }
        for (uint32_t o = 0; o < node->num_outputs; o++) {
          subgraph->values[node->outputs[o]].fp16_compatible = true;
        }
        break;
    }
  }

  // Replace FP32 Values in Nodes' inputs/outputs with FP16 Values.
  // FP32 Values that are not external inputs or outputs are converted to FP16 in-place,
  // for external inputs and outputs we create same-shaped FP16 Values and use those instead.
  const uint32_t num_original_values = subgraph->num_values;
  xnn_subgraph_analyze_consumers_and_producers(subgraph);
  for (uint32_t n = 0; n < num_original_values; n++) {
    struct xnn_value* value = &subgraph->values[n];
    value->fp16_id = XNN_INVALID_VALUE_ID;
    value->fp32_id = XNN_INVALID_VALUE_ID;
    if (value->fp16_compatible) {
      assert(value->data == NULL);
      assert(value->datatype == xnn_datatype_fp32);
      if (xnn_value_is_external(value)) {
        struct xnn_value* fp16_value = xnn_subgraph_new_internal_value(subgraph);

        // Recompute value due to potential reallocation in xnn_subgraph_new_internal_value
        value = &subgraph->values[n];
        xnn_value_copy(fp16_value, value);
        fp16_value->datatype = xnn_datatype_fp16;

        fp16_value->producer = value->producer;
        fp16_value->num_consumers = value->num_consumers;
        fp16_value->first_consumer = value->first_consumer;
        value->producer = XNN_INVALID_NODE_ID;
        value->num_consumers = 0;
        value->first_consumer = XNN_INVALID_NODE_ID;

        // Clear external input/output flags
        fp16_value->flags = 0;
        xnn_log_debug("FP16 rewrite: created FP16 tensor #%" PRIu32 " for FP32 tensor #%" PRIu32, fp16_value->id, n);

        value->fp16_id = fp16_value->id;
        fp16_value->fp32_id = n;
      } else {
        xnn_log_debug("FP16 rewrite: converted FP32 tensor #%" PRIu32 " to FP16", n);
        value->datatype = xnn_datatype_fp16;
      }
    }
  }
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    struct xnn_node* node = &subgraph->nodes[n];
    if (node->type == xnn_node_type_invalid) {
      // Node was fused away, skip.
      continue;
    }

    assert(node->compute_type == xnn_compute_type_fp32);
    node->compute_type = xnn_compute_type_fp16;
    if (node->type == xnn_node_type_static_constant_pad) {
      node->params.static_pad.padding_value =
        fp16_ieee_from_fp32_value(uint32_as_float(node->params.static_pad.padding_value));
    }
    for (uint32_t i = 0; i < node->num_inputs; i++) {
      const uint32_t fp16_id = subgraph->values[node->inputs[i]].fp16_id;
      if (fp16_id != XNN_INVALID_VALUE_ID) {
        assert(subgraph->values[fp16_id].fp32_id == node->inputs[i]);
        node->inputs[i] = fp16_id;
      }
    }
    for (uint32_t o = 0; o < node->num_outputs; o++) {
      const uint32_t fp16_id = subgraph->values[node->outputs[o]].fp16_id;
      if (fp16_id != XNN_INVALID_VALUE_ID) {
        assert(subgraph->values[fp16_id].fp32_id == node->outputs[o]);
        node->outputs[o] = fp16_id;
      }
    }
  }

  // Count the number of external inputs and outputs which require Convert nodes
  uint32_t num_external_inputs = 0;
  uint32_t num_external_outputs = 0;
  for (uint32_t n = 0; n < subgraph->num_nodes; n++) {
    const struct xnn_node* node = &subgraph->nodes[n];
    for (uint32_t i = 0; i < node->num_inputs; i++) {
      const struct xnn_value* value = &subgraph->values[node->inputs[i]];
      if (value->fp32_id != XNN_INVALID_VALUE_ID && value->first_consumer == n) {
        assert(value->data == NULL);
        assert(value->datatype == xnn_datatype_fp16);
        assert(subgraph->values[value->fp32_id].datatype == xnn_datatype_fp32);
        // This value isn't always an external input, it could be an external output of the current subgraph (due to
        // partition), and be simultaneously consumed by the current node.
        if (xnn_value_is_external_input(&subgraph->values[value->fp32_id])) {
          num_external_inputs += 1;
        }
      }
    }
    for (uint32_t o = 0; o < node->num_outputs; o++) {
      const struct xnn_value* value = &subgraph->values[node->outputs[o]];
      if (value->fp32_id != XNN_INVALID_VALUE_ID) {
        assert(value->datatype == xnn_datatype_fp16);
        assert(subgraph->values[value->fp32_id].datatype == xnn_datatype_fp32);
        assert(xnn_value_is_external_output(&subgraph->values[value->fp32_id]));
        num_external_outputs += 1;
      }
    }
  }
  xnn_log_debug("Discovered %"PRIu32" external inputs and %"PRIu32" external outputs",
    num_external_inputs, num_external_outputs);

  const uint32_t num_original_nodes = subgraph->num_nodes;
  xnn_subgraph_add_nodes(subgraph, num_external_inputs + num_external_outputs);
  struct xnn_node* output_node = subgraph->nodes + subgraph->num_nodes - 1;
  for (uint32_t n = num_original_nodes; n != 0; n--) {
    const struct xnn_node* node = &subgraph->nodes[n - 1];
    // Insert Convert nodes for outputs
    for (uint32_t o = 0; o < node->num_outputs; o++) {
      const struct xnn_value* value = &subgraph->values[node->outputs[o]];
      if (value->fp32_id != XNN_INVALID_VALUE_ID) {
        xnn_log_debug("Inserted FP16->FP32 Convert Node from tensor #%"PRIu32" to tensor #%"PRIu32,
          value->id, value->fp32_id);
        const uint32_t output_node_id = output_node->id;
        assert(output_node >= subgraph->nodes);
        xnn_node_clear(output_node);
        output_node->id = output_node_id;
        xnn_init_convert_node(output_node, xnn_compute_type_fp16_to_fp32, value->id, value->fp32_id, 0 /* flags */);
        output_node -= 1;
      }
    }
    // Move the Node to the new location
    if (output_node != node) {
      const uint32_t output_node_id = output_node->id;
      assert(output_node >= subgraph->nodes);
      memcpy(output_node, node, sizeof(struct xnn_node));
      output_node->id = output_node_id;
      output_node -= 1;
    }
    // Insert Convert nodes for inputs
    for (uint32_t i = 0; i < node->num_inputs; i++) {
      const struct xnn_value* value = &subgraph->values[node->inputs[i]];
      if (value->fp32_id != XNN_INVALID_VALUE_ID && value->first_consumer == n - 1) {
        // Only insert convert nodes if the value actually is an external input. This value could be an external output,
        // if that's the case, we have already inserted a convert node in loop above for outputs.
        if (xnn_value_is_external_input(&subgraph->values[value->fp32_id])) {
          xnn_log_debug("Inserted FP32->FP16 Convert Node from tensor #%"PRIu32" to tensor #%"PRIu32,
                        value->fp32_id, value->id);
          const uint32_t output_node_id = output_node->id;
          assert(output_node >= subgraph->nodes);
          xnn_node_clear(output_node);
          output_node->id = output_node_id;
          xnn_init_convert_node(output_node, xnn_compute_type_fp32_to_fp16, value->fp32_id, value->id, 0 /* flags */);
          output_node -= 1;
        }
      }
    }
  }

  return true;
}

enum xnn_status xnn_subgraph_fusion(
    xnn_subgraph_t subgraph)
{
  // Fuse Nodes where possible
  for (uint32_t i = 0; i < subgraph->num_values; i++) {
    struct xnn_value* value = &subgraph->values[i];
    if (value->num_consumers == 1) {
      const uint32_t producer_id = value->producer;
      if (producer_id == XNN_INVALID_NODE_ID) {
        continue;
      }
      assert(producer_id < subgraph->num_nodes);

      const uint32_t consumer_id = value->first_consumer;
      if (consumer_id == XNN_INVALID_NODE_ID) {
        continue;
      }
      assert(consumer_id < subgraph->num_nodes);

      struct xnn_node* producer = &subgraph->nodes[producer_id];
      assert(producer->type != xnn_node_type_invalid);
      struct xnn_node* consumer = &subgraph->nodes[consumer_id];
      assert(consumer->type != xnn_node_type_invalid);

      // Try to fuse Clamp Node upstream into producer Node
      if (consumer->type == xnn_node_type_clamp) {
        switch (producer->type) {
          case xnn_node_type_add2:
          case xnn_node_type_average_pooling_2d:
          case xnn_node_type_clamp:
          case xnn_node_type_convolution_2d:
          case xnn_node_type_divide:
          case xnn_node_type_deconvolution_2d:
          case xnn_node_type_depthwise_convolution_2d:
          case xnn_node_type_fully_connected:
          case xnn_node_type_multiply2:
          case xnn_node_type_max_pooling_2d:
          case xnn_node_type_subtract:
            xnn_log_info("fuse Clamp Node #%"PRIu32" into upstream Node #%"PRIu32, consumer_id, producer_id);
            assert(producer->num_outputs == 1);
            assert(consumer->num_inputs == 1);
            assert(consumer->num_outputs == 1);

            const uint32_t fused_output_id = consumer->outputs[0];
            assert(fused_output_id < subgraph->num_values);
            subgraph->values[fused_output_id].producer = producer_id;
            producer->outputs[0] = fused_output_id;

            producer->activation.output_min =
              math_max_f32(producer->activation.output_min, consumer->activation.output_min);
            producer->activation.output_max =
              math_min_f32(producer->activation.output_max, consumer->activation.output_max);

            xnn_node_clear(consumer);
            xnn_value_clear(value);
            break;
          default:
            break;
        }
      }
      // Try to fuse Constant Pad node downstream into [Depthwise] Convolution 2D Node
      if (producer->type == xnn_node_type_static_constant_pad) {
        assert(producer->num_inputs == 1);
        assert(producer->num_outputs == 1);
        const bool is_spatial_2d_padding = value->shape.num_dims == 4 &&
          (producer->params.static_pad.pre_paddings[0] | producer->params.static_pad.post_paddings[0] |
           producer->params.static_pad.pre_paddings[3] | producer->params.static_pad.post_paddings[3]) == 0;
        const enum xnn_datatype padding_datatype = subgraph->values[producer->outputs[0]].datatype;
        const uint32_t padding_value = producer->params.static_pad.padding_value;
        const bool is_zero_padding =
          (padding_datatype == xnn_datatype_fp32 && padding_value == 0) ||
          ((padding_datatype == xnn_datatype_qint8 || padding_datatype == xnn_datatype_quint8) &&
          padding_value == (uint32_t) (uint8_t) subgraph->values[producer->outputs[0]].quantization.zero_point);
        switch (consumer->type) {
          case xnn_node_type_convolution_2d:
            if (is_spatial_2d_padding && is_zero_padding && !(consumer->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING)) {
              xnn_log_info("fuse Constant Pad Node #%"PRIu32" into Convolution 2D Node #%"PRIu32,
                consumer_id, producer_id);
              assert(consumer->num_inputs >= 1);
              assert(consumer->inputs[0] == producer->outputs[0]);

              consumer->params.convolution_2d.input_padding_top    += producer->params.static_pad.pre_paddings[1];
              consumer->params.convolution_2d.input_padding_right  += producer->params.static_pad.post_paddings[2];
              consumer->params.convolution_2d.input_padding_bottom += producer->params.static_pad.post_paddings[1];
              consumer->params.convolution_2d.input_padding_left   += producer->params.static_pad.pre_paddings[2];

              consumer->inputs[0] = producer->inputs[0];

              const uint32_t fused_input_id = producer->inputs[0];
              assert(fused_input_id < subgraph->num_values);
              if (subgraph->values[fused_input_id].first_consumer == producer_id) {
                subgraph->values[fused_input_id].first_consumer = consumer_id;
              }

              xnn_node_clear(producer);
              xnn_value_clear(value);
            }
            break;
          case xnn_node_type_depthwise_convolution_2d:
            if (is_spatial_2d_padding && is_zero_padding && !(consumer->flags & XNN_FLAG_TENSORFLOW_SAME_PADDING)) {
              xnn_log_info("fuse Constant Pad Node #%"PRIu32" into Depthwise Convolution 2D Node #%"PRIu32,
                consumer_id, producer_id);
              assert(consumer->num_inputs >= 1);
              assert(consumer->inputs[0] == producer->outputs[0]);

              consumer->params.depthwise_convolution_2d.input_padding_top +=
                producer->params.static_pad.pre_paddings[1];
              consumer->params.depthwise_convolution_2d.input_padding_right +=
                producer->params.static_pad.post_paddings[2];
              consumer->params.depthwise_convolution_2d.input_padding_bottom +=
                producer->params.static_pad.post_paddings[1];
              consumer->params.depthwise_convolution_2d.input_padding_left +=
                producer->params.static_pad.pre_paddings[2];

              consumer->inputs[0] = producer->inputs[0];

              const uint32_t fused_input_id = producer->inputs[0];
              assert(fused_input_id < subgraph->num_values);
              if (subgraph->values[fused_input_id].first_consumer == producer_id) {
                subgraph->values[fused_input_id].first_consumer = consumer_id;
              }

              xnn_node_clear(producer);
              xnn_value_clear(value);
            }
            break;
          default:
            break;
        }
      }
    }
  }

  return xnn_status_success;
}

enum xnn_status xnn_subgraph_optimize(
  xnn_subgraph_t subgraph,
  uint32_t flags)
{
  xnn_subgraph_analyze_consumers_and_producers(subgraph);

  // Remove unreferenced values.
  for (uint32_t i = 0; i < subgraph->num_values; i++) {
    struct xnn_value* value = &subgraph->values[i];
    if (value->type == xnn_value_type_invalid) {
      continue;
    }

    if (!xnn_value_is_external_input(value) && value->num_consumers == 0) {
      xnn_value_clear(value);
    }
  }


  if (!(flags & XNN_FLAG_NO_OPERATOR_FUSION)) {
    xnn_subgraph_fusion(subgraph);
  }

  #if XNN_ENABLE_SPARSE
    if ((flags & XNN_FLAG_HINT_SPARSE_INFERENCE) && (xnn_params.init_flags & XNN_INIT_FLAG_CHW_OPT)) {
      xnn_subgraph_rewrite_for_nchw(subgraph);
    }
  #endif

  if ((flags & XNN_FLAG_FORCE_FP16_INFERENCE) && !(xnn_params.init_flags & XNN_INIT_FLAG_F16)) {
    xnn_log_error("failed to force FP16 inference: hardware supports neither native nor emulated FP16 operators");
    return xnn_status_unsupported_hardware;
  }
  #ifndef XNN_NO_F16_OPERATORS
    const bool try_native_fp16 =
      (flags & XNN_FLAG_HINT_FP16_INFERENCE) && (xnn_params.init_flags & XNN_INIT_FLAG_F16_NATIVE);
    const bool force_fp16 = (flags & XNN_FLAG_FORCE_FP16_INFERENCE);
    if (try_native_fp16 || force_fp16) {
      const bool fp16_rewrite_succeeded = xnn_subgraph_rewrite_for_fp16(subgraph);
      if (force_fp16 && !fp16_rewrite_succeeded) {
        xnn_log_error("failed to force FP16 inference: subgraph is incompatible with FP16 operators");
        return xnn_status_unsupported_parameter;
      }
    }
  #endif  // XNN_NO_F16_OPERATORS

  return xnn_status_success;
}

enum xnn_status xnn_delete_subgraph(
  xnn_subgraph_t subgraph)
{
  if (subgraph != NULL) {
    memset(subgraph->nodes, 0, sizeof(struct xnn_node) * subgraph->num_nodes);
    xnn_release_memory(subgraph->nodes);

    memset(subgraph->values, 0, sizeof(struct xnn_value) * subgraph->num_values);
    xnn_release_memory(subgraph->values);

    memset(subgraph, 0, sizeof(struct xnn_subgraph));
    xnn_release_memory(subgraph);
  }
  return xnn_status_success;
}