src/xnnpack/subgraph.h

// 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.

#pragma once

#include <stddef.h>
#include <stdint.h>

#include <xnnpack.h>

#define XNN_MAX_INPUTS 3
#define XNN_MAX_OUTPUTS 2

#define XNN_MAX_RUNTIME_INPUTS 2
#define XNN_MAX_RUNTIME_OUTPUTS 2

struct xnn_shape {
  size_t num_dims;
  size_t dim[XNN_MAX_TENSOR_DIMS];
};

enum xnn_value_type {
  xnn_value_type_invalid = 0,
  xnn_value_type_dense_tensor = 1,
};

/// Abstraction for a collections of elements produced and consumed by nodes.
struct xnn_value {
  /// Unique ID for the value.
  uint32_t id;
  /// Type of the collection of elements.
  ///
  /// Currently only dense tensors are supported.
  /// Other types (e.g. sparse tensors) might be supported in the future.
  enum xnn_value_type type;
  /// Type of elements in the collection.
  enum xnn_datatype datatype;
  /// Tensor shape.
  struct xnn_shape shape;
  /// Binary features of the tensor. Supported values are any combination of:
  /// - XNN_VALUE_FLAG_EXTERNAL_INPUT
  /// - XNN_VALUE_FLAG_EXTERNAL_OUTPUT
  uint32_t flags;
  /// Static initialization data. Must be null for non-static values.
  const void* data;
};

struct xnn_blob {
  /// Size in bytes.
  size_t size;
  /// Data pointer.
  void* data;
  bool external;
};

enum xnn_node_type {
  xnn_node_type_invalid = 0,
  xnn_node_type_add2,
  xnn_node_type_argmax_pooling_2d,
  xnn_node_type_average_pooling_2d,
  xnn_node_type_clamp,
  xnn_node_type_constant_pad,
  xnn_node_type_convolution_2d,
  xnn_node_type_deconvolution_2d,
  xnn_node_type_depthwise_convolution_2d,
  xnn_node_type_fully_connected,
  xnn_node_type_hardswish,
  xnn_node_type_multiply2,
  xnn_node_type_max_pooling_2d,
  xnn_node_type_prelu,
  xnn_node_type_sigmoid,
  xnn_node_type_softmax,
  xnn_node_type_unpooling_2d,
};

struct xnn_node {
  enum xnn_node_type type;
  uint32_t id;
  /// Static parameters of the operator node.
  union {
    struct {
      uint32_t input_padding_top;
      uint32_t input_padding_right;
      uint32_t input_padding_bottom;
      uint32_t input_padding_left;
      uint32_t kernel_height;
      uint32_t kernel_width;
      uint32_t subsampling_height;
      uint32_t subsampling_width;
      uint32_t dilation_height;
      uint32_t dilation_width;
      uint32_t groups;
      size_t group_input_channels;
      size_t group_output_channels;
    } convolution_2d;
    struct {
      uint32_t padding_top;
      uint32_t padding_right;
      uint32_t padding_bottom;
      uint32_t padding_left;
      uint32_t adjustment_height;
      uint32_t adjustment_width;
      uint32_t kernel_height;
      uint32_t kernel_width;
      uint32_t upsampling_height;
      uint32_t upsampling_width;
      uint32_t dilation_height;
      uint32_t dilation_width;
      uint32_t groups;
      size_t group_input_channels;
      size_t group_output_channels;
    } deconvolution_2d;
    struct {
      uint32_t input_padding_top;
      uint32_t input_padding_right;
      uint32_t input_padding_bottom;
      uint32_t input_padding_left;
      uint32_t kernel_height;
      uint32_t kernel_width;
      uint32_t subsampling_height;
      uint32_t subsampling_width;
      uint32_t dilation_height;
      uint32_t dilation_width;
      uint32_t depth_multiplier;
      size_t input_channels;
    } depthwise_convolution_2d;
    struct {
      uint32_t padding_top;
      uint32_t padding_right;
      uint32_t padding_bottom;
      uint32_t 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;
    } pooling_2d;
    struct {
      size_t pre_paddings[XNN_MAX_TENSOR_DIMS];
      size_t post_paddings[XNN_MAX_TENSOR_DIMS];
      uint32_t padding_value;
    } static_pad;
  } params;
  struct {
    float output_min;
    float output_max;
  } activation;
  /// Value IDs for node inputs.
  uint32_t inputs[XNN_MAX_INPUTS];
  uint32_t num_inputs;
  /// Value IDs for node outputs.
  uint32_t outputs[XNN_MAX_OUTPUTS];
  uint32_t num_outputs;
  uint32_t flags;
};

struct xnn_operator_data {
  xnn_operator_t op;
  size_t batch_size;
  size_t input_height;
  size_t input_width;
  struct xnn_shape shape1;
  struct xnn_shape shape2;
  size_t pre_paddings[XNN_MAX_TENSOR_DIMS];
  size_t post_paddings[XNN_MAX_TENSOR_DIMS];
  uint32_t adjustment_height;
  uint32_t adjustment_width;
  uint32_t inputs[XNN_MAX_RUNTIME_INPUTS];
  uint32_t outputs[XNN_MAX_RUNTIME_OUTPUTS];
};

struct xnn_subgraph {
  /// Number of Value IDs reserved for communication with external graph representation.
  /// Values created during subgraph transformation avoid using IDs in [0, reserved_value_ids-1] range.
  uint32_t external_value_ids;

  uint32_t num_reserved_values;
  uint32_t num_values;
  struct xnn_value* values;

  uint32_t num_reserved_nodes;
  uint32_t num_nodes;
  struct xnn_node* nodes;
};

/// Runtime is a combination of an execution plan for subgraph Nodes and a memory manager for subgraph Values.
struct xnn_runtime {
  uint32_t num_external_values;

  /// List of operators in the execution plan, in execution order.
  struct xnn_operator_data* ops;
  /// Number of operators in the execution plan.
  size_t num_ops;

  struct xnn_blob* blobs;
  size_t num_blobs;

  void* workspace;

  pthreadpool_t threadpool;
};

struct xnn_value* xnn_subgraph_new_internal_value(xnn_subgraph_t subgraph);

struct xnn_node* xnn_subgraph_new_node(xnn_subgraph_t subgraph);

size_t xnn_tensor_get_size(
  xnn_subgraph_t subgraph,
  uint32_t value_id);