public void processInstruction()

in src/main/java/org/apache/sysds/runtime/instructions/gpu/DnnGPUInstruction.java [720:888]
139 lines of code
51 McCabe index (conditional complexity)

	public void processInstruction(ExecutionContext ec) {
		if (instOpcode.equalsIgnoreCase("bias_add") || instOpcode.equalsIgnoreCase("bias_multiply")) {
			processBiasInstruction(instOpcode, ec);
			return;
		}
		else if (instOpcode.equalsIgnoreCase("relu_backward")) {
			processReLUBackwardInstruction(ec);
			return;
		}
		else if (instOpcode.equalsIgnoreCase("channel_sums")) {
			processChannelSumsInstruction(ec);
			return;
		}
		else if (instOpcode.equalsIgnoreCase("update_nesterov_x")) {
			processNesterovUpdateInstruction(ec);
			return;
		}
		else if (instOpcode.equalsIgnoreCase("lstm")) {
			processLstmInstruction(ec);
			return;
		}
		else if (instOpcode.equalsIgnoreCase("lstm_backward")) {
			processLstmBackwardInstruction(ec);
			return;
		}
		else if (instOpcode.equalsIgnoreCase("batch_norm2d")) {
			processBatchNorm2dInstruction(ec);
			return;
		}
		else if (instOpcode.equalsIgnoreCase("batch_norm2d_backward")) {
			processBatchNorm2dBackwardInstruction(ec);
			return;
		}
		else if (instOpcode.equalsIgnoreCase("batch_norm2d_test")) {
			processBatchNorm2dTestInstruction(ec);
			return;
		}
		else if (instOpcode.equalsIgnoreCase("batch_norm2d_train")) {
			processBatchNorm2dTrainInstruction(ec);
			return;
		}
		
		GPUStatistics.incrementNoOfExecutedGPUInst();
					
		int pad_h = getScalarInput(ec, _padding, 0);
		int pad_w = getScalarInput(ec, _padding, 1);
		int stride_h = getScalarInput(ec, _stride, 0);
		int stride_w = getScalarInput(ec, _stride, 1);

		int N = getScalarInput(ec, _input_shape, 0); //N
		int C = getScalarInput(ec, _input_shape, 1); //C
		int H = getScalarInput(ec, _input_shape, 2); //Hin
		int W = getScalarInput(ec, _input_shape, 3); //Win

		int K = getScalarInput(ec, _filter_shape, 0); //F = nrow(W)
		
		int R = getScalarInput(ec, _filter_shape, 2); //Hf
		int S = getScalarInput(ec, _filter_shape, 3); //Wf
		
		int P = (int) DnnUtils.getP(H, R, stride_h, pad_h);
		int Q = (int) DnnUtils.getQ(W, S, stride_w, pad_w);
		
		if (instOpcode.equalsIgnoreCase("conv2d")) {
			MatrixObject image = getMatrixInputForGPUInstruction(ec, _input1.getName());
			MatrixObject filter = getMatrixInputForGPUInstruction(ec, _input2.getName());

			if(image.getNumRows() != N || image.getNumColumns() != C*H*W) 
				throw new DMLRuntimeException("Incorrect dimensions for image in conv2d");
			if(filter.getNumRows() != K || filter.getNumColumns() != C*R*S) 
				throw new DMLRuntimeException("Incorrect dimensions for filter in conv2d");
			
			MatrixObject out = getDenseMatrixOutputForGPUInstruction(ec, _output.getName(), N, K * P * Q);
			
			LibMatrixCuDNN.conv2d(ec.getGPUContext(0), getExtendedOpcode(), image, filter, out, N, C, H, W,
					K, R, S, pad_h, pad_w, stride_h, stride_w, P, Q, _intermediateMemoryBudget);
		}
		else if (instOpcode.equalsIgnoreCase("conv2d_bias_add")) {
			MatrixObject image = getMatrixInputForGPUInstruction(ec, _input1.getName()); //X
			MatrixObject bias = getMatrixInputForGPUInstruction(ec, _input2.getName());  //b
			MatrixObject filter = getMatrixInputForGPUInstruction(ec, _input3.getName()); //W

			if(image.getNumRows() != N || image.getNumColumns() != C*H*W) 
				throw new DMLRuntimeException("Incorrect dimensions for image in conv2d");
			if(filter.getNumRows() != K || filter.getNumColumns() != C*R*S) 
				throw new DMLRuntimeException("Incorrect dimensions for filter in conv2d");
			
			MatrixObject out = getDenseMatrixOutputForGPUInstruction(ec, _output.getName(), N, K * P * Q);
			
			LibMatrixCuDNN.conv2dBiasAdd(ec.getGPUContext(0), getExtendedOpcode(), image, bias, filter, out, N, C, H, W,
						K, R, S, pad_h, pad_w, stride_h, stride_w, P, Q, _intermediateMemoryBudget);
		}
		else if (instOpcode.equalsIgnoreCase("conv2d_backward_filter")) {
			MatrixObject image = getMatrixInputForGPUInstruction(ec, _input1.getName());
			MatrixObject dout = getMatrixInputForGPUInstruction(ec, _input2.getName());

			if(image.getNumRows() != N || image.getNumColumns() != C*H*W) 
				throw new DMLRuntimeException("Incorrect dimensions for image in conv2d_backward_filter");
			if(dout.getNumRows() != N || dout.getNumColumns() != K*P*Q) 
				throw new DMLRuntimeException("Incorrect dimensions for dout in conv2d_backward_filter: " + 
						dout.getNumRows() + " != " +  N + " || " + dout.getNumColumns() + " != " + K*P*Q);
			
			MatrixObject out = getDenseMatrixOutputForGPUInstruction(ec, _output.getName(), K, C * R * S);
			
			LibMatrixCuDNN.conv2dBackwardFilter(ec.getGPUContext(0), getExtendedOpcode(), image, dout, out, N, C, H, W,
					K, R, S, pad_h, pad_w, stride_h, stride_w, P, Q, _intermediateMemoryBudget);
			// TODO: For now always copy the device data to host
			// ec.gpuCtx.copyDeviceToHost(outputBlock);
		}
		else if (instOpcode.equalsIgnoreCase("conv2d_backward_data")) {
			MatrixObject filter = getMatrixInputForGPUInstruction(ec, _input1.getName());
			MatrixObject dout = getMatrixInputForGPUInstruction(ec, _input2.getName());

			if(filter.getNumRows() != K || filter.getNumColumns() != C*R*S) 
				throw new DMLRuntimeException("Incorrect dimensions for filter in convolution_backward_data");
			if(dout.getNumRows() != N || dout.getNumColumns() != K*P*Q) 
				throw new DMLRuntimeException("Incorrect dimensions for dout in conv2d_backward_data: " + 
						dout.getNumRows() + " != " +  N + " || " + dout.getNumColumns() + " != " + K*P*Q);
			
			MatrixObject out = getDenseMatrixOutputForGPUInstruction(ec, _output.getName(), N, C * H * W);
			
			LibMatrixCuDNN.conv2dBackwardData(ec.getGPUContext(0), getExtendedOpcode(), filter, dout, out, N, C, H, W,
					K, R, S, pad_h, pad_w, stride_h, stride_w, P, Q, _intermediateMemoryBudget);
		}
		else if (instOpcode.equalsIgnoreCase("maxpooling") || instOpcode.equalsIgnoreCase("avgpooling")) {
			MatrixObject image = getMatrixInputForGPUInstruction(ec, _input1.getName());

			if(image.getNumRows() != N || image.getNumColumns() != C*H*W) 
				throw new DMLRuntimeException("Incorrect dimensions for image in maxpooling: " + 
						image.getNumRows() + " != " +  N + " || " + image.getNumColumns() + " != " + C*H*W);
			
			MatrixObject out = getDenseMatrixOutputForGPUInstruction(ec, _output.getName(), N, C * P * Q);
			PoolingType poolType = instOpcode.equalsIgnoreCase("maxpooling") ? PoolingType.MAX : PoolingType.AVG;
			LibMatrixCuDNN.pooling(ec.getGPUContext(0), getExtendedOpcode(), image, out, N, C, H, W,
					K, R, S, pad_h, pad_w, stride_h, stride_w, P, Q, poolType, _intermediateMemoryBudget);
		}
		else if (instOpcode.equalsIgnoreCase("maxpooling_backward") || instOpcode.equalsIgnoreCase("avgpooling_backward")) {
			MatrixObject image = getMatrixInputForGPUInstruction(ec, _input1.getName());
			MatrixObject dout = getMatrixInputForGPUInstruction(ec, _input2.getName());
			MatrixObject maxPoolOutput = _input3 != null ? getMatrixInputForGPUInstruction(ec, _input3.getName()) : null;
			if(dout.getNumRows() != N || dout.getNumColumns() != C*P*Q) 
				throw new DMLRuntimeException("Incorrect dimensions for dout in maxpooling_backward");
			if(image.getNumRows() != N || image.getNumColumns() != C*H*W) 
				throw new DMLRuntimeException("Incorrect dimensions for image in maxpooling_backward: " + 
						image.getNumRows() + " != " +  N + " || " + image.getNumColumns() + " != " + K*P*Q);
			
			MatrixObject out = getDenseMatrixOutputForGPUInstruction(ec, _output.getName(), N, C * H * W);
			PoolingType poolType = instOpcode.equalsIgnoreCase("maxpooling_backward") ? PoolingType.MAX : PoolingType.AVG;
			LibMatrixCuDNN.poolingBackward(ec.getGPUContext(0), getExtendedOpcode(), image, dout, maxPoolOutput, out, N, C, H, W,
					K, R, S, pad_h, pad_w, stride_h, stride_w, P, Q, poolType, _intermediateMemoryBudget);
		}
		else {
			throw new DMLRuntimeException("Unsupported GPU context for " + instOpcode);
		}
		
		// release inputs/outputs
		ec.releaseMatrixInputForGPUInstruction(_input1.getName());
		
		boolean isPool = instOpcode.equalsIgnoreCase("maxpooling") || instOpcode.equalsIgnoreCase("avgpooling");
		boolean isPoolBackward = instOpcode.equalsIgnoreCase("maxpooling_backward") || instOpcode.equalsIgnoreCase("avgpooling_backward");

		if ( !isPool )
			ec.releaseMatrixInputForGPUInstruction(_input2.getName());

		if (instOpcode.equalsIgnoreCase("conv2d_bias_add") || 
			(isPoolBackward && _input3 != null))
			ec.releaseMatrixInputForGPUInstruction(_input3.getName());

		ec.releaseMatrixOutputForGPUInstruction(_output.getName());
	}