graphengine/ge/host_kernels/range_kernel.cc

/**
 * Copyright 2020 Huawei Technologies Co., Ltd
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include "host_kernels/range_kernel.h"

#include <memory>
#include <set>

#include "common/debug/log.h"
#include "common/fp16_t.h"
#include "common/types.h"
#include "common/util.h"
#include "framework/common/debug/ge_log.h"
#include "framework/common/ge_inner_error_codes.h"
#include "graph/utils/type_utils.h"
#include "inc/kernel_factory.h"

namespace ge {
namespace {
constexpr size_t kRangeInputNum = 3;
constexpr uint32_t kRangeDimNum = 0;
const std::set<DataType> kRangeSupportedType = {DT_INT32, DT_FLOAT};
}  // namespace

Status RangeKernel::Compute(const OpDescPtr op_desc_ptr, const std::vector<ConstGeTensorPtr> &input,
                            std::vector<GeTensorPtr> &v_output) {
  GELOGD("RangeKernel in");
  if (op_desc_ptr == nullptr) {
    GELOGE(PARAM_INVALID, "Parameter's invalid, input opDescPtr is nullptr.");
    return PARAM_INVALID;
  }
  Status ret = RangeCheck(input);
  if (ret != SUCCESS) {
    return ret;
  }

  GeTensorPtr output_ptr = MakeShared<GeTensor>(op_desc_ptr->GetOutputDesc(0));
  if (output_ptr == nullptr) {
    GELOGE(MEMALLOC_FAILED, "Make shared failed");
    return MEMALLOC_FAILED;
  }

  ConstGeTensorPtr start = input.at(0);
  ConstGeTensorPtr limit = input.at(1);
  ConstGeTensorPtr delta = input.at(2);
  DataType data_type = delta->GetTensorDesc().GetDataType();
  if (data_type == DT_FLOAT) {
    if (GetRange(*reinterpret_cast<const float *>(start->GetData().data()),
                 *reinterpret_cast<const float *>(limit->GetData().data()),
                 *reinterpret_cast<const float *>(delta->GetData().data()), output_ptr) != SUCCESS) {
      return PARAM_INVALID;
    }
  } else if (data_type == DT_INT32) {
    if (GetRange(*reinterpret_cast<const int32_t *>(start->GetData().data()),
                 *reinterpret_cast<const int32_t *>(limit->GetData().data()),
                 *reinterpret_cast<const int32_t *>(delta->GetData().data()), output_ptr) != SUCCESS) {
      return PARAM_INVALID;
    }
  }

  output_ptr->MutableTensorDesc().SetDataType(data_type);
  v_output.push_back(output_ptr);
  return SUCCESS;
}

Status RangeKernel::RangeCheck(const std::vector<ConstGeTensorPtr> &input) {
  // check input number
  if (input.size() != kRangeInputNum) {
    GELOGI("The number of input for Range must be %zu.", kRangeInputNum);
    return NOT_CHANGED;
  }

  ConstGeTensorPtr start = input.at(0);
  ConstGeTensorPtr limit = input.at(1);
  ConstGeTensorPtr delta = input.at(2);

  GE_CHECK_NOTNULL(start);
  GE_CHECK_NOTNULL(limit);
  GE_CHECK_NOTNULL(delta);
  // check whether there is data in Tensor
  if (start->GetData().size() == 0 || limit->GetData().size() == 0 || delta->GetData().size() == 0) {
    GELOGI("Check data size fail. start: %zu, limit: %zu, delta: %zu", start->GetData().size(), limit->GetData().size(),
           delta->GetData().size());
    return NOT_CHANGED;
  }

  // check whether the data types are the same
  DataType type = start->GetTensorDesc().GetDataType();
  if ((type != limit->GetTensorDesc().GetDataType()) || (type != delta->GetTensorDesc().GetDataType())) {
    GELOGI("Data type of inputs for Range not matched.");
    return NOT_CHANGED;
  }

  // check whether are all scalars
  size_t range_dim = static_cast<size_t>(kRangeDimNum);
  bool all_scalar = (start->GetTensorDesc().MutableShape().GetDimNum() == range_dim) &&
                    (limit->GetTensorDesc().MutableShape().GetDimNum() == range_dim) &&
                    (delta->GetTensorDesc().MutableShape().GetDimNum() == range_dim);
  if (!all_scalar) {
    GELOGI("Inputs for Range are not all scalars.");
    return NOT_CHANGED;
  }

  // check if input data type is supported
  if (kRangeSupportedType.find(type) == kRangeSupportedType.end()) {
    GELOGI("Range does not support this Data type: %s", TypeUtils::DataTypeToSerialString(type).c_str());
    return NOT_CHANGED;
  }

  return SUCCESS;
}

template <typename T>
Status RangeKernel::GetRange(const T start, const T limit, const T delta, GeTensorPtr &output) {
  // check whether start, limit, delta is valid
  if (delta == 0) {
    GELOGE(PARAM_INVALID, "Requires delta != 0");
    return PARAM_INVALID;
  }
  if (start > limit && delta > 0) {
    GELOGE(PARAM_INVALID, "Requires start <= limit when delta > 0");
    return PARAM_INVALID;
  }
  if (start < limit && delta < 0) {
    GELOGE(PARAM_INVALID, "Requires start >= limit when delta < 0");
    return PARAM_INVALID;
  }

  int64_t size = (std::is_integral<T>::value ? ((std::abs(limit - start) + std::abs(delta) - 1) / std::abs(delta))
                                             : std::ceil(std::abs((limit - start) / delta)));
  output->MutableTensorDesc().SetShape(GeShape());  // when size is 0

  if (size > 0) {
    unique_ptr<T[]> buf(new (std::nothrow) T[size]);
    if (buf == nullptr) {
      GELOGE(MEMALLOC_FAILED, "New buf failed.");
      return MEMALLOC_FAILED;
    }

    T val = start;
    for (int64_t i = 0; i < size; ++i) {
      buf[i] = val;
      val += delta;
    }
    if (output->SetData(reinterpret_cast<uint8_t *>(buf.get()), size * sizeof(T)) != GRAPH_SUCCESS) {
      GELOGW("GetRange: SetData failed");
    }
    output->MutableTensorDesc().SetShape(GeShape({size}));
  }

  return SUCCESS;
}

REGISTER_KERNEL(RANGE, RangeKernel);
}  // namespace ge