com.simiacryptus.mindseye.layers.cudnn.BandAvgReducerLayer Maven / Gradle / Ivy

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/*
 * Copyright (c) 2019 by Andrew Charneski.
 *
 * The author licenses this file to you 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.
 */

package com.simiacryptus.mindseye.layers.cudnn;

import com.google.gson.JsonObject;
import com.simiacryptus.mindseye.lang.*;
import com.simiacryptus.mindseye.lang.cudnn.*;
import jcuda.jcudnn.*;

import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.UUID;
import java.util.stream.Stream;

/**
 * Similar to the pooling key, but the pool size is always the png size. The output dimensions are always 1x1xN.
 */
@SuppressWarnings("serial")
public class BandAvgReducerLayer extends LayerBase implements MultiPrecision {

  private Precision precision = Precision.Double;
  private double alpha = 1.0;

  /**
   * Instantiates a new Pooling key.
   */
  public BandAvgReducerLayer() {
    super();
  }

  /**
   * Instantiates a new Pooling key.
   *
   * @param json the json
   */
  protected BandAvgReducerLayer(@Nonnull final JsonObject json) {
    super(json);
    precision = Precision.valueOf(json.get("precision").getAsString());
    alpha = json.get("alpha").getAsDouble();
  }

  /**
   * From json pooling key.
   *
   * @param json the json
   * @param rs   the rs
   * @return the pooling key
   */
  public static BandAvgReducerLayer fromJson(@Nonnull final JsonObject json, Map rs) {
    return new BandAvgReducerLayer(json);
  }

  /**
   * Gets compatibility key.
   *
   * @return the compatibility key
   */
  @Nonnull
  public Layer getCompatibilityLayer() {
    throw new RuntimeException("Not Implemented");
  }

  @Nullable
  @Override
  public Result evalAndFree(final Result... inObj) {
    if (!CudaSystem.isEnabled()) return getCompatibilityLayer().evalAndFree(inObj);
    final Result input = inObj[0];
    TensorList inputData = input.getData();
    @Nonnull final int[] inputSize = inputData.getDimensions();
    int length = inputData.length();
    if (length <= 0) throw new AssertionError();
    if (Tensor.length(inputData.getDimensions()) <= 0) return input;
    final int bands = inputSize[2];
    CudaTensorList result = CudaSystem.run(gpu -> {
      CudaTensor inputTensor = gpu.getTensor(inputData, precision, MemoryType.Device, false);
      @Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(precision, length, bands, 1, 1);
      long size = (long) precision.size * outputDescriptor.nStride * length;
      @Nonnull final CudaMemory outputPtr = gpu.allocate(size, MemoryType.Managed, true);
      CudaResource reduceTensorDescriptor = gpu.cudnnCreateReduceTensorDescriptor(
          cudnnReduceTensorOp.CUDNN_REDUCE_TENSOR_AVG, precision.code, cudnnNanPropagation.CUDNN_NOT_PROPAGATE_NAN,
          cudnnReduceTensorIndices.CUDNN_REDUCE_TENSOR_NO_INDICES, cudnnIndicesType.CUDNN_32BIT_INDICES);

      CudaMemory inputMemory = inputTensor.getMemory(gpu);
      @Nonnull final CudaMemory workspacePtr = gpu.allocate(inputMemory.size, MemoryType.Device, true);
      @Nonnull final CudaMemory indexPtr = gpu.allocate(12 * length, MemoryType.Device, false);

      gpu.cudnnReduceTensor(reduceTensorDescriptor.getPtr(),
          indexPtr.getPtr(), indexPtr.size, workspacePtr.getPtr(), workspacePtr.size,
          precision.getPointer(alpha), inputTensor.descriptor.getPtr(), inputMemory.getPtr(),
          precision.getPointer(0.0), outputDescriptor.getPtr(), outputPtr.getPtr());
      outputPtr.dirty();
      inputMemory.dirty();

      Stream.of(inputMemory, inputTensor, reduceTensorDescriptor, workspacePtr, indexPtr, inputData).forEach(ReferenceCounting::freeRef);
      return CudaTensorList.wrap(CudaTensor.wrap(outputPtr, outputDescriptor, precision), length, new int[]{1, 1, bands}, precision);
    });
    int pixels = inputSize[0] * inputSize[1];
    return new Result(result, (DeltaSet ctx, TensorList delta) -> {
      TensorList passback;
      passback = TensorArray.wrap(delta.stream().map(x -> {
        Tensor tensor = new Tensor(inputSize[0], inputSize[1], inputSize[2])
            .setByCoord(c -> x.get(c.getCoords()[2]) * alpha / pixels);
        x.freeRef();
        return tensor;
      }).toArray(i -> new Tensor[i]));
//      passback = CudaSystem.generate(gpu -> {
//        CudaTensor deltaTensor = gpu.getTensor(evalInputDelta, precision, MemoryType.Device, true);
//        @Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(precision,
//          length, inputSize[2], inputSize[1], inputSize[0]);
//        @Nonnull final CudaMemory outputPtr = gpu.allocate((long) precision.size * outputDescriptor.nStride * length, MemoryType.Device, true);
//        CudaMemory deltaMemory = deltaTensor.getMemory(gpu);
//        @Nonnull final CudaDevice.CudaTensorDescriptor inputDescriptor = gpu.newTensorDescriptor(precision,
//          1, 1, inputSize[1], inputSize[0]);
//        for(int batch=0;batch resources, DataSerializer dataSerializer) {
    @Nonnull final JsonObject json = super.getJsonStub();
    json.addProperty("alpha", alpha);
    json.addProperty("precision", precision.name());
    return json;
  }

  @Override
  public Precision getPrecision() {
    return precision;
  }

  @Nonnull
  @Override
  public BandAvgReducerLayer setPrecision(final Precision precision) {
    this.precision = precision;
    return this;
  }

  @Nonnull
  @Override
  public List state() {
    return Arrays.asList();
  }

  /**
   * Gets alphaList.
   *
   * @return the alphaList
   */
  public double getAlpha() {
    return alpha;
  }

  /**
   * Sets alphaList.
   *
   * @param alpha the alphaList
   * @return the alphaList
   */
  public BandAvgReducerLayer setAlpha(double alpha) {
    this.alpha = alpha;
    return this;
  }
}