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65 lines
3 KiB
Python
65 lines
3 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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#
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# The utility functions for GPU computation
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#
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import numpy as np
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from ..util import gpu_init
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try:
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from pycuda.reduction import ReductionKernel
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from pycuda.elementwise import ElementwiseKernel
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# log|A| for A is a low triangle matrix
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# logDiagSum(A, A.shape[0]+1)
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logDiagSum = ReductionKernel(np.float64, neutral="0", reduce_expr="a+b", map_expr="i%step==0?log(x[i]):0", arguments="double *x, int step")
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strideSum = ReductionKernel(np.float64, neutral="0", reduce_expr="a+b", map_expr="i%step==0?x[i]:0", arguments="double *x, int step")
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# np.trace(np.dot(A,B)) (also equivalent to (A*B.T).sum() ) A - a1 x a2, B - a2 x a1
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traceDot = ReductionKernel(np.float64, neutral="0", reduce_expr="a+b", map_expr="A[i]*B[(i%a1)*a2+i/a1]", arguments="double *A, double *B, int a1, int a2")
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#=======================================================================================
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# Element-wise functions
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#=======================================================================================
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# log(X)
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log = ElementwiseKernel("double *in, double *out", "out[i] = log(in[i])", "log_element")
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# log(1.0-X)
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logOne = ElementwiseKernel("double *in, double *out", "out[i] = log(1.-in[i])", "logOne_element")
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# multiplication with broadcast on the last dimension (out = shorter[:,None]*longer)
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mul_bcast = ElementwiseKernel("double *out, double *shorter, double *longer, int shorter_size", "out[i] = longer[i]*shorter[i%shorter_size]", "mul_bcast")
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# multiplication with broadcast on the first dimension (out = shorter[None,:]*longer)
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mul_bcast_first = ElementwiseKernel("double *out, double *shorter, double *longer, int first_dim", "out[i] = longer[i]*shorter[i/first_dim]", "mul_bcast")
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# sum through the middle dimension (size_2) of a 3D matrix (size_1, size_2, size_3)
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sum_axis = ElementwiseKernel("double *out, double *in, int size_1, int size_2", "out[i] += sum_axis_element(in, size_1, size_2, i)", "sum_axis",preamble="""
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__device__ double sum_axis_element(double *in, int size_1, int size_2, int idx)
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{
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int k = idx/size_1;
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int i = idx%size_1;
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double sum=0;
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for(int j=0;j<size_2;j++) {
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sum += in[(k*size_2+j)*size_1+i];
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}
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return sum;
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}
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""")
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# the outer product between two vectors (out = np.dot(v1,v2.T))
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outer_prod = ElementwiseKernel("double *out, double *v1, double *v2, int v1_size", "out[i] = v1[i%v1_size]*v2[i/v1_size]", "outer_prod")
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# the outer product between two vectors (out = np.einsum('na,nb->nab',m1,m2) a=dim1, b=dim2 )
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join_prod = ElementwiseKernel("double *out, double *m1, double *m2, int dim1, int dim2", "out[i] = m1[(i%dim1)*dim1+(i%(dim1*dim2))/dim1]*m2[(i%dim1)*dim1+i/(dim1*dim2)]", "join_prod")
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except:
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pass
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