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提交 1cd88000 authored 作者: Frederic's avatar Frederic
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上级 aa024e24
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正在显示 包含 122 行增加 和 68 行删除
theano/sparse/sandbox/sp2.py
浏览文件 @ 1cd88000
import numpy
import scipy.sparse
from theano import gof, tensor, scalar
from theano.tensor import blas
from theano.sparse.basic import (
as_sparse_variable, SparseType, add_s_s, neg,
mul_s_s, mul_s_d,
mul_s_s, mul_s_d, dot,
CSMProperties, CSM, register_specialize,
_is_sparse_variable, CSC, CSR,
csm_data, csm_indices, csm_indptr, csm_shape,
csm_properties, csm_data, csm_indices, csm_indptr, csm_shape,
_is_sparse)
......@@ -183,40 +184,51 @@ class MulSDCSC(gof.Op):
raise NotImplementedError('Complex types are not supported for b')
return """
if (%(_b)s->nd != 2) {PyErr_SetString(PyExc_NotImplementedError, "rank(b) != 2"); %(fail)s;}
if (%(_data)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(data) != 1"); %(fail)s;}
if (%(_indices)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(indices) != 1"); %(fail)s;}
if (%(_indptr)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(indptr) != 1"); %(fail)s;}
if (%(_b)s->nd != 2) {
PyErr_SetString(PyExc_NotImplementedError, "rank(b) != 2");
%(fail)s;}
if (%(_data)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(data) != 1");
%(fail)s;}
if (%(_indices)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(indices) != 1");
%(fail)s;}
if (%(_indptr)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(indptr) != 1");
%(fail)s;}
if( %(_indices)s->descr->type_num != PyArray_INT32) {
PyErr_SetString(PyExc_NotImplementedError, "C"); %(fail)s;}
if( %(_indptr)s->descr->type_num != PyArray_INT32)
{PyErr_SetString(PyExc_NotImplementedError, "D"); %(fail)s;}
if (!%(_zout)s)
{
%(_zout)s = (PyArrayObject*) PyArray_SimpleNew(1, %(_indices)s->dimensions, %(_b)s->descr->type_num);
%(_zout)s = (PyArrayObject*) PyArray_SimpleNew(1,
%(_indices)s->dimensions, %(_b)s->descr->type_num);
}
if (%(_zout)s->dimensions[0] != %(_indices)s->dimensions[0])
{
PyErr_SetString(PyExc_NotImplementedError, "somehow _zout got the wrong size.. and I don't know how to resize it.");
PyErr_SetString(PyExc_NotImplementedError,
"somehow _zout got the wrong size.. and I don't know how to resize it.");
%(fail)s;
}
{ //makes it compile even though labels jump over variable definitions.
{ //makes it compile even though labels jump over variable definitions.
const npy_intp nnz = %(_indices)s->dimensions[0];
const npy_intp N = %(_indptr)s->dimensions[0]-1; //TODO: error checking with this
//TODO: error checking with this
const npy_intp N = %(_indptr)s->dimensions[0]-1;
const dtype_%(_data)s * const __restrict__ data = (dtype_%(_data)s*)%(_data)s->data;
const npy_int32 * const __restrict__ indptr = (npy_int32 *)%(_indptr)s->data;
const npy_int32 * const __restrict__ indices = (npy_int32 *)%(_indices)s->data;
dtype_%(_zout)s * const __restrict__ zout = (dtype_%(_zout)s*)%(_zout)s->data;
const npy_intp Sb = %(_b)s->strides[0];
// loop over columns
for (npy_int32 j = 0; j < N; ++j)
{
......@@ -225,10 +237,10 @@ class MulSDCSC(gof.Op):
{
// extract row index of non-null value
npy_int32 i = indices[i_idx];
// extract i-th row of dense matrix
const dtype_%(_b)s* __restrict__ b_row = (dtype_%(_b)s*)(%(_b)s->data + Sb * i);
// write resulting gradient to sparse output
zout[i_idx] = data[i_idx] * b_row[j];
}
......@@ -262,52 +274,63 @@ class MulSDCSR(gof.Op):
raise NotImplementedError('Complex types are not supported for b')
return """
if (%(_b)s->nd != 2) {PyErr_SetString(PyExc_NotImplementedError, "rank(b) != 2"); %(fail)s;}
if (%(_data)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(data) != 1"); %(fail)s;}
if (%(_indices)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(indices) != 1"); %(fail)s;}
if (%(_indptr)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(indptr) != 1"); %(fail)s;}
if (%(_b)s->nd != 2) {
PyErr_SetString(PyExc_NotImplementedError, "rank(b) != 2");
%(fail)s;}
if (%(_data)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(data) != 1");
%(fail)s;}
if (%(_indices)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(indices) != 1");
%(fail)s;}
if (%(_indptr)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(indptr) != 1");
%(fail)s;}
if( %(_indices)s->descr->type_num != PyArray_INT32) {
PyErr_SetString(PyExc_NotImplementedError, "C"); %(fail)s;}
if( %(_indptr)s->descr->type_num != PyArray_INT32)
{PyErr_SetString(PyExc_NotImplementedError, "D"); %(fail)s;}
if (!%(_zout)s)
{
%(_zout)s = (PyArrayObject*) PyArray_SimpleNew(1, %(_indices)s->dimensions, %(_b)s->descr->type_num);
%(_zout)s = (PyArrayObject*) PyArray_SimpleNew(1,
%(_indices)s->dimensions, %(_b)s->descr->type_num);
}
if (%(_zout)s->dimensions[0] != %(_indices)s->dimensions[0])
{
PyErr_SetString(PyExc_NotImplementedError, "somehow _zout got the wrong size.. and I don't know how to resize it.");
PyErr_SetString(PyExc_NotImplementedError,
"somehow _zout got the wrong size.. and I don't know how to resize it.");
%(fail)s;
}
{ //makes it compile even though labels jump over variable definitions.
{ //makes it compile even though labels jump over variable definitions.
const npy_intp nnz = %(_indices)s->dimensions[0];
const npy_intp N = %(_indptr)s->dimensions[0]-1; //TODO: error checking with this
//TODO: error checking with this
const npy_intp N = %(_indptr)s->dimensions[0]-1;
const dtype_%(_data)s * const __restrict__ data = (dtype_%(_data)s*)%(_data)s->data;
const npy_int32 * const __restrict__ indptr = (npy_int32 *)%(_indptr)s->data;
const npy_int32 * const __restrict__ indices = (npy_int32 *)%(_indices)s->data;
dtype_%(_zout)s * const __restrict__ zout = (dtype_%(_zout)s*)%(_zout)s->data;
const npy_intp Sb = %(_b)s->strides[0];
// loop over columns
for (npy_int32 j = 0; j < N; ++j)
{
// extract i-th row of dense matrix
const dtype_%(_b)s* __restrict__ b_row = (dtype_%(_b)s*)(%(_b)s->data + Sb * j);
// for each non-null value in the sparse column
for (npy_int32 i_idx = indptr[j]; i_idx < indptr[j+1]; ++i_idx)
{
// extract row index of non-null value
npy_int32 i = indices[i_idx];
// write resulting gradient to sparse output
zout[i_idx] = data[i_idx] * b_row[i];
}
......@@ -550,42 +573,57 @@ class StrucutedAddSVCSR(gof.Op):
raise NotImplementedError('Complex types are not supported for b')
return """
if (%(_b)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(b) != 1"); %(fail)s;}
if (%(_data)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(data) != 1"); %(fail)s;}
if (%(_indices)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(indices) != 1"); %(fail)s;}
if (%(_indptr)s->nd != 1) {PyErr_SetString(PyExc_NotImplementedError, "rank(indptr) != 1"); %(fail)s;}
if (%(_b)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(b) != 1");
%(fail)s;
}
if (%(_data)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(data) != 1");
%(fail)s;
}
if (%(_indices)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(indices) != 1");
%(fail)s;
}
if (%(_indptr)s->nd != 1) {
PyErr_SetString(PyExc_NotImplementedError, "rank(indptr) != 1");
%(fail)s;
}
if( %(_indices)s->descr->type_num != PyArray_INT32) {
PyErr_SetString(PyExc_NotImplementedError, "C"); %(fail)s;}
if( %(_indptr)s->descr->type_num != PyArray_INT32)
{PyErr_SetString(PyExc_NotImplementedError, "D"); %(fail)s;}
if (!%(_zout)s)
{
%(_zout)s = (PyArrayObject*) PyArray_SimpleNew(1, %(_indices)s->dimensions, %(_b)s->descr->type_num);
%(_zout)s = (PyArrayObject*) PyArray_SimpleNew(1,
%(_indices)s->dimensions, %(_b)s->descr->type_num);
}
if (%(_zout)s->dimensions[0] != %(_indices)s->dimensions[0])
{
PyErr_SetString(PyExc_NotImplementedError, "somehow _zout got the wrong size.. and I don't know how to resize it.");
PyErr_SetString(PyExc_NotImplementedError,
"somehow _zout got the wrong size.. and I don't know how to resize it.");
%(fail)s;
}
{ //makes it compile even though labels jump over variable definitions.
{ //makes it compile even though labels jump over variable definitions.
const npy_intp nnz = %(_indices)s->dimensions[0];
const npy_intp N = %(_indptr)s->dimensions[0]-1; //TODO: error checking with this
//TODO: error checking with this
const npy_intp N = %(_indptr)s->dimensions[0]-1;
const dtype_%(_data)s * const __restrict__ data = (dtype_%(_data)s*)%(_data)s->data;
const npy_int32 * const __restrict__ indptr = (npy_int32 *)%(_indptr)s->data;
const npy_int32 * const __restrict__ indices = (npy_int32 *)%(_indices)s->data;
const dtype_%(_b)s* __restrict__ Db = (dtype_%(_b)s*)%(_b)s->data;
dtype_%(_zout)s * const __restrict__ zout = (dtype_%(_zout)s*)%(_zout)s->data;
const npy_intp Sb = %(_b)s->strides[0] / %(_b)s->descr->elsize;
// loop over columns
for (npy_int32 j = 0; j < N; ++j)
{
......@@ -594,7 +632,7 @@ class StrucutedAddSVCSR(gof.Op):
{
// extract row index of non-null value
npy_int32 i = indices[i_idx];
// write resulting gradient to sparse output
zout[i_idx] = data[i_idx] + Db[i * Sb];
}
......@@ -671,6 +709,7 @@ class SamplingDot(gof.op.Op):
if not _is_sparse_variable(p):
raise TypeError(p)
#TODO: use it.
dtype_out = scalar.upcast(x.type.dtype, y.type.dtype, p.type.dtype)
return gof.Apply(self, [x, y, p], [p.type()])
......@@ -790,24 +829,36 @@ class SamplingDotCsr(gof.Op):
[]).dtype_specs()[-1]
rval = """
if (%(x)s->nd != 2) {PyErr_SetString(PyExc_NotImplementedError, "rank(x) != 2"); %(fail)s;}
if (%(y)s->nd != 2) {PyErr_SetString(PyExc_NotImplementedError, "rank(y) != 2"); %(fail)s;}
if (%(x)s->nd != 2) {
PyErr_SetString(PyExc_NotImplementedError, "rank(x) != 2"); %(fail)s;}
if (%(y)s->nd != 2) {
PyErr_SetString(PyExc_NotImplementedError, "rank(y) != 2"); %(fail)s;}
if (%(x)s->descr->type_num != %(typenum_x)s) {
PyErr_SetString(PyExc_NotImplementedError, "Invalid type for x"); %(fail)s;}
PyErr_SetString(PyExc_NotImplementedError,
"Invalid type for x");
%(fail)s;}
if (%(y)s->descr->type_num != %(typenum_y)s) {
PyErr_SetString(PyExc_NotImplementedError, "Invalid type for y"); %(fail)s;}
PyErr_SetString(PyExc_NotImplementedError,
"Invalid type for y");
%(fail)s;}
if (%(p_data)s->descr->type_num != %(typenum_p)s) {
PyErr_SetString(PyExc_NotImplementedError, "Invalid type for pattern"); %(fail)s;}
if (%(x)s->dimensions[1] != %(y)s->dimensions[1])
{PyErr_SetString(PyExc_NotImplementedError, "x's number of columns doesn't match y's rows! Note: sampling_dot is different from dot because y is assumed to be transposed."); %(fail)s;}
if (%(y)s->dimensions[0] != ((npy_int32 *)%(p_ncols)s->data)[0] || %(x)s->dimensions[0] != (%(p_ptr)s->dimensions[0] - 1))
{PyErr_SetString(PyExc_NotImplementedError, "The dimension of the pattern and the output must match"); %(fail)s;}
PyErr_SetString(PyExc_NotImplementedError,
"Invalid type for pattern");
%(fail)s;}
if (%(x)s->dimensions[1] != %(y)s->dimensions[1]) {
PyErr_SetString(PyExc_NotImplementedError,
"x's number of columns doesn't match y's rows! Note: sampling_dot is different from dot because y is assumed to be transposed.");
%(fail)s;}
if (%(y)s->dimensions[0] != ((npy_int32 *)%(p_ncols)s->data)[0] ||
%(x)s->dimensions[0] != (%(p_ptr)s->dimensions[0] - 1))
{PyErr_SetString(PyExc_NotImplementedError,
"The dimension of the pattern and the output must match"); %(fail)s;}
// Allocate output
if (!%(z_data)s
|| (%(z_data)s->dimensions[0] != %(p_data)s->dimensions[0])
......@@ -815,7 +866,8 @@ class SamplingDotCsr(gof.Op):
{Py_XDECREF(%(z_data)s);}
npy_intp dims[] = {0};
dims[0] = %(p_data)s->dimensions[0];
%(z_data)s = (PyArrayObject*) PyArray_SimpleNew(1, dims, %(typenum_zd)s);
%(z_data)s = (PyArrayObject*) PyArray_SimpleNew(1, dims,
%(typenum_zd)s);
}
if (!%(z_ind)s
|| (%(z_ind)s->dimensions[0] != %(p_ind)s->dimensions[0])
......@@ -823,7 +875,8 @@ class SamplingDotCsr(gof.Op):
{Py_XDECREF(%(z_ind)s);}
npy_intp dims[] = {0};
dims[0] = %(p_ind)s->dimensions[0];
%(z_ind)s = (PyArrayObject*) PyArray_SimpleNew(1, dims, %(typenum_zi)s);
%(z_ind)s = (PyArrayObject*) PyArray_SimpleNew(1, dims,
%(typenum_zi)s);
}
if (!%(z_ptr)s
|| (%(z_ptr)s->dimensions[0] != %(p_ptr)s->dimensions[0])
......@@ -831,15 +884,16 @@ class SamplingDotCsr(gof.Op):
{Py_XDECREF(%(z_ptr)s);}
npy_intp dims[] = {0};
dims[0] = %(p_ptr)s->dimensions[0];
%(z_ptr)s = (PyArrayObject*) PyArray_SimpleNew(1, dims, %(typenum_zp)s);
%(z_ptr)s = (PyArrayObject*) PyArray_SimpleNew(1, dims,
%(typenum_zp)s);
}
{
// Product of MxK and NxK, output MxN
npy_intp M = %(x)s->dimensions[0];
npy_intp N = %(y)s->dimensions[0];
npy_intp K = %(y)s->dimensions[1];
// pointers to access actual data in the arrays passed as params.
const dtype_%(x)s* __restrict__ Dx = (dtype_%(x)s*)%(x)s->data;
const dtype_%(y)s* __restrict__ Dy = (dtype_%(y)s*)%(y)s->data;
......
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