src/tensor.c

// 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 <assert.h>
#include <math.h>
#include <stddef.h>
#include <stdint.h>
#include <stdlib.h>

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


enum xnn_status xnn_define_tensor_value(
    xnn_subgraph_t subgraph,
    enum xnn_datatype datatype,
    size_t num_dims,
    const size_t* dims,
    const void* data,
    uint32_t external_id,
    uint32_t flags,
    uint32_t* id_out)
{
  if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
    xnn_log_error("failed to create Dense Tensor value: XNNPACK is not initialized");
    return xnn_status_uninitialized;
  }

  if (external_id != XNN_INVALID_VALUE_ID && external_id >= subgraph->external_value_ids) {
    xnn_log_error(
      "failed to create Dense Tensor value: "
      "external ID %" PRIu32 " exceeds the number of reserved external IDs in subgraph (%" PRIu32 ")",
      external_id, subgraph->external_value_ids);
    return xnn_status_invalid_parameter;
  }

  if (num_dims > XNN_MAX_TENSOR_DIMS) {
    xnn_log_error("failed to create Dense Tensor value: num of dimensions exceeds XNNPACK limit (%d)",
      XNN_MAX_TENSOR_DIMS);
    return xnn_status_unsupported_parameter;
  }

  switch (datatype) {
    case xnn_datatype_fp32:
    case xnn_datatype_fp16:
      break;
    default:
      xnn_log_error("failed to create Dense Tensor value: unsupported datatype %s (%d)",
        xnn_datatype_to_string(datatype), datatype);
      return xnn_status_unsupported_parameter;
  }

  struct xnn_value* value = subgraph->values + external_id;
  if (external_id == XNN_INVALID_VALUE_ID) {
    value = xnn_subgraph_new_internal_value(subgraph);
    if (value == NULL) {
      return xnn_status_out_of_memory;
    }
  }
  value->type = xnn_value_type_dense_tensor;
  value->datatype = datatype;
  value->shape.num_dims = num_dims;
  memcpy(value->shape.dim, dims, num_dims * sizeof(size_t));
  value->flags = flags;
  value->data = data;

  *id_out = value->id;
  return xnn_status_success;
}

enum xnn_status xnn_define_quantized_tensor_value(
    xnn_subgraph_t subgraph,
    enum xnn_datatype datatype,
    int32_t zero_point,
    float scale,
    size_t num_dims,
    const size_t* dims,
    const void* data,
    uint32_t external_id,
    uint32_t flags,
    uint32_t* id_out)
{
  if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
    xnn_log_error("failed to create Quantized Dense Tensor value: XNNPACK is not initialized");
    return xnn_status_uninitialized;
  }

  if (external_id != XNN_INVALID_VALUE_ID && external_id >= subgraph->external_value_ids) {
    xnn_log_error(
      "failed to create Quantized Dense Tensor value: "
      "external ID %" PRIu32 " exceeds the number of reserved external IDs in subgraph (%" PRIu32 ")",
      external_id, subgraph->external_value_ids);
    return xnn_status_invalid_parameter;
  }

  if (num_dims > XNN_MAX_TENSOR_DIMS) {
    xnn_log_error(
      "failed to create Quantized Dense Tensor value: num of dimensions exceeds XNNPACK limit (%d)",
      XNN_MAX_TENSOR_DIMS);
    return xnn_status_unsupported_parameter;
  }

  switch (datatype) {
    case xnn_datatype_qint8:
      if ((int32_t) (int8_t) zero_point != zero_point) {
        xnn_log_error(
          "failed to create Quantized Dense Tensor value: invalid zero point %" PRId32" outside the [-128, 127] range",
          zero_point);
        return xnn_status_invalid_parameter;
      }
      break;
    case xnn_datatype_quint8:
      if ((int32_t) (uint8_t) zero_point != zero_point) {
        xnn_log_error(
          "failed to create Quantized Dense Tensor value: invalid zero point %" PRId32" outside the [0, 255] range",
          zero_point);
        return xnn_status_invalid_parameter;
      }
      break;
    case xnn_datatype_qint32:
      if (zero_point != 0) {
        xnn_log_error(
          "failed to create Quantized Dense Tensor value: invalid non-zero zero point %" PRId32,
          zero_point);
        return xnn_status_invalid_parameter;
      }
      break;
    default:
      xnn_log_error("failed to create Quantized Dense Tensor value: unsupported datatype %s (%d)",
        xnn_datatype_to_string(datatype), datatype);
      return xnn_status_unsupported_parameter;
  }

  if (scale <= 0.0f || !isnormal(scale)) {
    xnn_log_error(
      "failed to create Quantized Dense Tensor value with %.7g scale: scale must be finite, normalized, and positive",
      scale);
    return xnn_status_invalid_parameter;
  }

  struct xnn_value* value = subgraph->values + external_id;
  if (external_id == XNN_INVALID_VALUE_ID) {
    value = xnn_subgraph_new_internal_value(subgraph);
    if (value == NULL) {
      return xnn_status_out_of_memory;
    }
  }
  value->type = xnn_value_type_dense_tensor;
  value->datatype = datatype;
  value->quantization.zero_point = zero_point;
  value->quantization.scale = scale;
  value->shape.num_dims = num_dims;
  memcpy(value->shape.dim, dims, num_dims * sizeof(size_t));
  value->flags = flags;
  value->data = data;

  *id_out = value->id;
  return xnn_status_success;
}

enum xnn_status xnn_define_channelwise_quantized_tensor_value(
    xnn_subgraph_t subgraph,
    enum xnn_datatype datatype,
    const float* scale,
    size_t num_dims,
    size_t channel_dim,
    const size_t* dims,
    const void* data,
    uint32_t external_id,
    uint32_t flags,
    uint32_t* id_out)
{
  if ((xnn_params.init_flags & XNN_INIT_FLAG_XNNPACK) == 0) {
    xnn_log_error("failed to create Channelwise Quantized Dense Tensor value: XNNPACK is not initialized");
    return xnn_status_uninitialized;
  }

  if (external_id != XNN_INVALID_VALUE_ID && external_id >= subgraph->external_value_ids) {
    xnn_log_error(
      "failed to create Channelwise Quantized Dense Tensor value: "
      "external ID %" PRIu32 " exceeds the number of reserved external IDs in subgraph (%" PRIu32 ")",
      external_id, subgraph->external_value_ids);
    return xnn_status_invalid_parameter;
  }

  if (num_dims == 0) {
    xnn_log_error(
      "failed to create Channelwise Quantized Dense Tensor value: no channel dimension exists");
    return xnn_status_invalid_parameter;
  }

  if (num_dims > XNN_MAX_TENSOR_DIMS) {
    xnn_log_error(
      "failed to create Channelwise Quantized Dense Tensor value: num of dimensions exceeds XNNPACK limit (%d)",
      XNN_MAX_TENSOR_DIMS);
    return xnn_status_unsupported_parameter;
  }

  if (channel_dim >= num_dims) {
    xnn_log_error(
      "failed to create Channelwise Quantized Dense Tensor value: "
      "channel dimension index %zu is out of range for %zu-dimensional tensor",
      channel_dim, num_dims);
    return xnn_status_invalid_parameter;
  }

  switch (datatype) {
    case xnn_datatype_qcint8:
    case xnn_datatype_qcint32:
      break;
    default:
      xnn_log_error("failed to create Channelwise Quantized Dense Tensor value: unsupported datatype %s (%d)",
        xnn_datatype_to_string(datatype), datatype);
      return xnn_status_unsupported_parameter;
  }

  const size_t channels = dims[0];
  for (size_t channel = 0; channel < channels; channel++) {
    if (scale[channel] <= 0.0f || !isnormal(scale[channel])) {
      xnn_log_error(
        "failed to create Channelwise Quantized Dense Tensor value with %.7g scale in channel #%zu: "
        "scale must be finite, normalized, and positive",
        scale[channel], channel);
      return xnn_status_invalid_parameter;
    }
  }

  struct xnn_value* value = subgraph->values + external_id;
  if (external_id == XNN_INVALID_VALUE_ID) {
    value = xnn_subgraph_new_internal_value(subgraph);
    if (value == NULL) {
      return xnn_status_out_of_memory;
    }
  }
  value->type = xnn_value_type_dense_tensor;
  value->datatype = datatype;
  value->quantization.zero_point = 0;
  value->quantization.channelwise_scale = scale;
  value->quantization.channel_dimension = channel_dim;
  value->shape.num_dims = num_dims;
  memcpy(value->shape.dim, dims, num_dims * sizeof(size_t));
  value->flags = flags;
  value->data = data;

  *id_out = value->id;
  return xnn_status_success;
}

size_t xnn_tensor_get_size(
  xnn_subgraph_t subgraph,
  uint32_t value_id)
{
  assert(value_id < subgraph->num_values);

  const struct xnn_value* value = subgraph->values + value_id;
  assert(value->type == xnn_value_type_dense_tensor);
  assert(value->datatype != xnn_datatype_invalid);

  size_t size = 0;
  switch (value->datatype) {
    case xnn_datatype_fp16:
      size = 2;
      break;
    case xnn_datatype_fp32:
      size = 4;
      break;
    case xnn_datatype_qint8:
    case xnn_datatype_quint8:
    case xnn_datatype_qcint8:
      size = 1;
      break;
    case xnn_datatype_qint32:
    case xnn_datatype_qcint32:
      size = 4;
      break;
    case xnn_datatype_invalid:
      XNN_UNREACHABLE;
  }

  for (size_t i = 0; i < value->shape.num_dims; i++) {
    size *= value->shape.dim[i];
  }

  return size;
}

size_t xnn_shape_multiply_all_dims(
  const struct xnn_shape shape[restrict XNN_MIN_ELEMENTS(1)])
{
  size_t batch_size = 1;
  for (size_t i = 0; i < shape->num_dims; i++) {
    batch_size *= shape->dim[i];
  }
  return batch_size;
}

size_t xnn_shape_multiply_non_channel_dims(
  const struct xnn_shape shape[restrict XNN_MIN_ELEMENTS(1)])
{
  size_t batch_size = 1;
  for (size_t i = 0; i + 1 < shape->num_dims; i++) {
    batch_size *= shape->dim[i];
  }
  return batch_size;
}