com.simiacryptus.mindseye.layers.cudnn.AvgReducerLayer 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.lang.ref.ReferenceCounting;
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.IntStream;
import java.util.stream.Stream;

@SuppressWarnings("serial")
public class AvgReducerLayer extends LayerBase implements MultiPrecision {

  private Precision precision = CudaSettings.INSTANCE().defaultPrecision;

  public AvgReducerLayer() {
    super();
  }

  protected AvgReducerLayer(@Nonnull final JsonObject json) {
    super(json);
    precision = Precision.valueOf(json.get("precision").getAsString());
  }

  public static AvgReducerLayer fromJson(@Nonnull final JsonObject json, Map rs) {
    return new AvgReducerLayer(json);
  }

  @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];
    final TensorList inputData = input.getData();
    @Nonnull final int[] inputSize = inputData.getDimensions();
    int length = inputData.length();

    CudaTensorList result = CudaSystem.run(gpu -> {
      CudaTensor inputTensor = gpu.getTensor(inputData, precision, MemoryType.Device, false);
      inputData.freeRef();
      CudaMemory inputMemory = inputTensor.getMemory(gpu);

      @Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(precision, length, 1, 1, 1);
      long size = (long) precision.size * outputDescriptor.nStride * length;
      @Nonnull final CudaMemory outputMemory = gpu.allocate(size, MemoryType.Managed.ifEnabled(), 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);

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

      //outputPtr.synchronize();
      gpu.cudnnReduceTensor(reduceTensorDescriptor.getPtr(),
          indexPtr.getPtr(), indexPtr.size, workspacePtr.getPtr(), workspacePtr.size,
          precision.getPointer(1.0), inputTensor.descriptor.getPtr(), inputMemory.getPtr(),
          precision.getPointer(0.0), outputDescriptor.getPtr(), outputMemory.getPtr());
      outputMemory.dirty();
      inputMemory.dirty();

      Stream.of(inputTensor, inputMemory, reduceTensorDescriptor, workspacePtr, indexPtr).forEach(ReferenceCounting::freeRef);
      return CudaTensorList.wrap(CudaTensor.wrap(outputMemory, outputDescriptor, precision), length, new int[]{1, 1, 1}, precision);
    });

    return new Result(result, (DeltaSet ctx, TensorList delta) -> {

      // Not supported by CuDNN?
//      CudaTensorList passback = CudaSystem.generate(gpu -> {
//        CudaTensor deltaTensor = gpu.getTensor(evalInputDelta, precision, MemoryType.Device, false);
//        CudaMemory deltaMemory = deltaTensor.getMemory(gpu);
//
//        @Nonnull final CudaDevice.CudaTensorDescriptor passbackDescriptor1 = gpu.newTensorDescriptor(
//          precision, length, inputSize[2], inputSize[1], inputSize[0]
//        );
//        @Nonnull final CudaMemory passbackPtr1 = gpu.allocate((long) precision.size * passbackDescriptor1.nStride * length, MemoryType.Device, false);
//        gpu.cudnnAddTensor(precision.getPointer(1.0), deltaTensor.descriptor.getPtr(), deltaMemory.getPtr(),
//          precision.getPointer(1.0), passbackDescriptor1.getPtr(), passbackPtr1.getPtr());
//        passbackPtr1.dirty();
//
//        Stream.of(deltaTensor, deltaMemory, passbackDescriptor1, passbackPtr1).forEach(ReferenceCounting::freeRef);
//        return CudaTensorList.wrap(CudaTensor.wrap(passbackPtr1, passbackDescriptor1, precision), length, inputSize, precision);
//      });

      input.accumulate(ctx, TensorArray.wrap(IntStream.range(0, length).mapToObj(i -> {
        Tensor tensor = delta.get(i);
        double v = (double) tensor.get(0) / Tensor.length(inputSize);
        Tensor tensor1 = new Tensor(inputSize).setAll(v);
        tensor.freeRef();
        return tensor1;
      }).toArray(i -> new Tensor[i])));
      delta.freeRef();
    }) {
      @Override
      protected void _free() {
        super._free();
        input.freeRef();
      }
    };
  }

  @Nonnull
  @Override
  public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
    @Nonnull final JsonObject json = super.getJsonStub();
    json.addProperty("precision", precision.name());
    return json;
  }


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

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

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

}