public void Execute(VectorExecutionOptions options) { var codeBuilder = new CUDACodeBuilder(); var block = _builder.ToBlock(); codeBuilder.GenerateCode(block); }
/// <summary> /// Performs reverse-mode (adjoint) automatic differentiation /// </summary> /// <param name="builder"></param> /// <param name="dependentVariable"></param> /// <param name="independentVariables"></param> public static void Differentiate(BlockExpressionBuilder builder, out IList <Expression> derivativeExpressions, VectorParameterExpression dependentVariable, params VectorParameterExpression[] independentVariables) { if (independentVariables.Length == 0) { derivativeExpressions = null; return; } var block = builder.ToBlock(); // change the numbering of variables; arguments first, then locals List <int>[] jLookup; Function[] f; // we want a list of functions which can be unary or binary (in principle high order if it makes sense) and a look-up // each function is associated with a parameter, which is either an argument or a local // for each function index we return a list of the indices of the functions that reference the parameter GetFunctions(block, out f, out jLookup); int N = dependentVariable.Index; bool[] derivativeRequired; int[] derivativeExpressionIndex; IdentifyNeccesaryDerivatives(N, jLookup, independentVariables, out derivativeRequired, out derivativeExpressionIndex); // the list of operations needed to calculate the derivatives (*all* derivatives) derivativeExpressions = new Expression[independentVariables.Length]; var dxNdx = new Expression[N + 1]; dxNdx[N] = new ConstantExpression <double>(1.0); for (int i = N - 1; i >= 0; i--) { if (!derivativeRequired[i]) { continue; } // dxN / dxi // need to find all operation indices j such that p(j) contains i // that is, all functions that have xi as an argument (these must therefore have a higher index than i) VectorParameterExpression total = new ConstantExpression <double>(0); var xi = f[i].Parameter; //need sum of dfj/dxi dXN/dxj foreach (var j in jLookup[i]) { var fj = f[j]; var dfjdxi = Differentiate(fj, xi, builder); // dfj/dxi var dXNdxj = dxNdx[j]; // dXN/dxj var product = builder.AddProductExpression(dfjdxi, dXNdxj); total = builder.AddAdditionExpression(total, product); } dxNdx[i] = total; int targetIndex = derivativeExpressionIndex[i]; if (targetIndex != -1) { derivativeExpressions[targetIndex] = total; } } }
/// <summary> /// Performs reverse-mode (adjoint) automatic differentiation /// </summary> /// <param name="builder"></param> /// <param name="dependentVariable"></param> /// <param name="independentVariables"></param> public static void Differentiate(BlockExpressionBuilder builder, out IList<Expression> derivativeExpressions, VectorParameterExpression dependentVariable, params VectorParameterExpression[] independentVariables) { if (independentVariables.Length == 0) { derivativeExpressions = null; return; } var block = builder.ToBlock(); // change the numbering of variables; arguments first, then locals List<int>[] jLookup; Function[] f; // we want a list of functions which can be unary or binary (in principle high order if it makes sense) and a look-up // each function is associated with a parameter, which is either an argument or a local // for each function index we return a list of the indices of the functions that reference the parameter GetFunctions(block, out f, out jLookup); int N = dependentVariable.Index; bool[] derivativeRequired; int[] derivativeExpressionIndex; IdentifyNeccesaryDerivatives(N, jLookup, independentVariables, out derivativeRequired, out derivativeExpressionIndex); // the list of operations needed to calculate the derivatives (*all* derivatives) derivativeExpressions = new Expression[independentVariables.Length]; var dxNdx = new Expression[N + 1]; dxNdx[N] = new ConstantExpression<double>(1.0); for (int i = N - 1; i >= 0; i--) { if (!derivativeRequired[i]) continue; // dxN / dxi // need to find all operation indices j such that p(j) contains i // that is, all functions that have xi as an argument (these must therefore have a higher index than i) VectorParameterExpression total = new ConstantExpression<double>(0); var xi = f[i].Parameter; //need sum of dfj/dxi dXN/dxj foreach (var j in jLookup[i]) { var fj = f[j]; var dfjdxi = Differentiate(fj, xi, builder); // dfj/dxi var dXNdxj = dxNdx[j]; // dXN/dxj var product = builder.AddProductExpression(dfjdxi, dXNdxj); total = builder.AddAdditionExpression(total, product); } dxNdx[i] = total; int targetIndex = derivativeExpressionIndex[i]; if (targetIndex != -1) derivativeExpressions[targetIndex] = total; } }