com.simiacryptus.mindseye.layers.cudnn.NProductLayer 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 com.simiacryptus.mindseye.layers.java.ProductInputsLayer;
import jcuda.jcudnn.JCudnn;
import jcuda.jcudnn.cudnnOpTensorDescriptor;
import jcuda.jcudnn.cudnnOpTensorOp;

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 NProductLayer extends LayerBase implements MultiPrecision {

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

  public NProductLayer() {
  }

  protected NProductLayer(@Nonnull final JsonObject id) {
    super(id);
    this.precision = Precision.valueOf(id.getAsJsonPrimitive("precision").getAsString());
  }

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

  @Nonnull
  public Layer getCompatibilityLayer() {
    return this.as(ProductInputsLayer.class);
  }


  @Nullable
  @Override
  public Result evalAndFree(@Nonnull final Result... inObj) {
    if (!CudaSystem.isEnabled()) return getCompatibilityLayer().evalAndFree(inObj);
    if (inObj.length <= 1) {
      throw new IllegalArgumentException("inObj.length=" + inObj.length);
    }
    @Nonnull final int[] dimensions = inObj[0].getData().getDimensions();
    final int length = inObj[0].getData().length();
    if (3 != dimensions.length) {
      throw new IllegalArgumentException("dimensions=" + Arrays.toString(dimensions));
    }
    for (int i = 1; i < inObj.length; i++) {
      TensorList data = inObj[i].getData();
      if (Tensor.length(dimensions) != Tensor.length(data.getDimensions())) {
        throw new IllegalArgumentException(Arrays.toString(dimensions) + " != " + Arrays.toString(data.getDimensions()));
      }
    }
    return new Result(CudaSystem.run(gpu -> {
      @Nonnull final CudaResource opDescriptor = gpu.newOpDescriptor(cudnnOpTensorOp.CUDNN_OP_TENSOR_MUL, precision);
      @Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(precision,
          length, dimensions[2], dimensions[1], dimensions[0],
          dimensions[2] * dimensions[1] * dimensions[0], dimensions[1] * dimensions[0], dimensions[0], 1);
      @Nonnull final TensorList result1 = Arrays.stream(inObj).map(x -> {
        TensorList data = x.getData();
        data.addRef();
        return data;
      }).reduce((l, r) -> {
        @Nullable final CudaTensor lPtr = gpu.getTensor(l, precision, MemoryType.Device, false);
        @Nullable final CudaTensor rPtr = gpu.getTensor(r, precision, MemoryType.Device, false);
        //assert lPtr.memory.size == rPtr.memory.size;
        @Nonnull final CudaMemory outputPtr = gpu.allocate((long) outputDescriptor.nStride * length * precision.size, MemoryType.Device, true);
        CudaMemory lPtrMemory = lPtr.getMemory(gpu);
        CudaMemory rPtrMemory = rPtr.getMemory(gpu);
        CudaSystem.handle(JCudnn.cudnnOpTensor(gpu.handle, opDescriptor.getPtr(),
            precision.getPointer(1.0), lPtr.descriptor.getPtr(), lPtrMemory.getPtr(),
            precision.getPointer(1.0), rPtr.descriptor.getPtr(), rPtrMemory.getPtr(),
            precision.getPointer(0.0), outputDescriptor.getPtr(), outputPtr.getPtr()));
        lPtrMemory.dirty();
        rPtrMemory.dirty();
        outputPtr.dirty();
        lPtrMemory.freeRef();
        rPtrMemory.freeRef();
        Arrays.stream(new ReferenceCounting[]{lPtr, rPtr, l, r}).forEach(ReferenceCounting::freeRef);
        outputDescriptor.addRef();
        return CudaTensorList.wrap(CudaTensor.wrap(outputPtr, outputDescriptor, precision), length, dimensions, precision);
      }).get();
      Arrays.stream(new ReferenceCounting[]{opDescriptor, outputDescriptor}).forEach(ReferenceCounting::freeRef);
      return result1;
    }, Arrays.stream(inObj).map(Result::getData).toArray()), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList delta) -> {
      for (int index = 0; index < inObj.length; index++) {
        final Result input = inObj[index];
        if (input.isAlive()) {
          final int _index = index;
          @Nonnull TensorList data = IntStream.range(0, inObj.length).mapToObj(i -> {
            TensorList tensorList = i == _index ? delta : inObj[i].getData();
            tensorList.addRef();
            return tensorList;
          }).reduce((l, r) -> {
            return CudaSystem.run(gpu -> {
              @Nonnull final CudaResource opDescriptor = gpu.newOpDescriptor(cudnnOpTensorOp.CUDNN_OP_TENSOR_MUL, precision);
              @Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(precision, length, dimensions[2], dimensions[1], dimensions[0], dimensions[2] * dimensions[1] * dimensions[0], dimensions[1] * dimensions[0], dimensions[0], 1);

              @Nullable final CudaTensor lPtr = gpu.getTensor(l, precision, MemoryType.Device, false);
              @Nullable final CudaTensor rPtr = gpu.getTensor(r, precision, MemoryType.Device, false);
              //assert lPtr.memory.size == rPtr.memory.size;
              @Nonnull final CudaMemory outputPtr = gpu.allocate((long) outputDescriptor.nStride * length * precision.size, MemoryType.Device, true);
              CudaMemory lPtrMemory = lPtr.getMemory(gpu);
              CudaMemory rPtrMemory = rPtr.getMemory(gpu);
              CudaSystem.handle(JCudnn.cudnnOpTensor(gpu.handle, opDescriptor.getPtr(),
                  precision.getPointer(1.0), lPtr.descriptor.getPtr(), lPtrMemory.getPtr(),
                  precision.getPointer(1.0), rPtr.descriptor.getPtr(), rPtrMemory.getPtr(),
                  precision.getPointer(0.0), outputDescriptor.getPtr(), outputPtr.getPtr()));
              lPtrMemory.dirty();
              rPtrMemory.dirty();
              outputPtr.dirty();
              lPtrMemory.freeRef();
              rPtrMemory.freeRef();
              Stream.of(lPtr, rPtr, opDescriptor, l, r).forEach(ReferenceCounting::freeRef);
              return CudaTensorList.wrap(CudaTensor.wrap(outputPtr, outputDescriptor, precision), length, dimensions, precision);
            }, l, r);
          }).get();
          input.accumulate(buffer, data);
        }
      }
      delta.freeRef();
    }) {

      @Override
      public final void accumulate(DeltaSet buffer, TensorList delta) {
        getAccumulator().accept(buffer, delta);
      }

      @Override
      protected void _free() {
        Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
        for (int i = 0; i < inObj.length; i++) {
          inObj[i].getData().freeRef();
        }
      }


      @Override
      public boolean isAlive() {
        for (@Nonnull final Result element : inObj)
          if (element.isAlive()) {
            return true;
          }
        return false;
      }

    };
  }

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

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

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

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