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pytensor
提交 c1ccd518 authored 作者: James Bergstra's avatar James Bergstra
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removed REAL from nnet.py, using dtype_%(varname)s approach instead

上级 c3f2fa12
隐藏空白字符变更
内嵌 并排
正在显示 包含 38 行增加 和 42 行删除
theano/tensor/nnet.py
浏览文件 @ c1ccd518
......@@ -77,7 +77,6 @@ pprint.assign(softplus, printing.FunctionPrinter('softplus'))
# TENSOR OPS
#
class SoftmaxWithBias(gof.Op):
"""
An L{Op} for the output of neural-net multiclass classifiers.
......@@ -159,12 +158,12 @@ class SoftmaxWithBias(gof.Op):
PyErr_SetString(PyExc_ValueError, "b not 1d tensor");
%(fail)s;
}
if ((%(x)s->descr->type_num != PyArray_DOUBLE)&&(%(x)s->descr->type_num != PyArray_DOUBLE))
if ((%(x)s->descr->type_num != PyArray_DOUBLE)&&(%(x)s->descr->type_num != PyArray_FLOAT))
{
PyErr_SetString(PyExc_TypeError, "a not float");
%(fail)s;
}
if ((%(b)s->descr->type_num != PyArray_DOUBLE) && (%(b)s->descr->type_num != PyArray_DOUBLE))
if ((%(b)s->descr->type_num != PyArray_DOUBLE) && (%(b)s->descr->type_num != PyArray_FLOAT))
{
PyErr_SetString(PyExc_TypeError, "b not float");
%(fail)s;
......@@ -195,22 +194,22 @@ class SoftmaxWithBias(gof.Op):
double sum = 0.0;
bool discount_max = false;
const REAL* __restrict__ x_i = (REAL*)(%(x)s->data + %(x)s->strides[0] * i);
const REAL* __restrict__ b_i = (REAL*)(%(b)s->data);
REAL* __restrict__ sm_i = (REAL*)(%(sm)s->data + %(sm)s->strides[0] * i);
const dtype_%(x)s* __restrict__ x_i = (dtype_%(x)s*)(%(x)s->data + %(x)s->strides[0] * i);
const dtype_%(b)s* __restrict__ b_i = (dtype_%(b)s*)(%(b)s->data);
dtype_%(sm)s* __restrict__ sm_i = (dtype_%(sm)s*)(%(sm)s->data + %(sm)s->strides[0] * i);
"""
inside_row_loop = """
npy_intp Sx = %(x)s->strides[1]/sizeof(REAL);
npy_intp Sb = %(b)s->strides[0]/sizeof(REAL);
npy_intp Ssm = %(sm)s->strides[1]/sizeof(REAL);
npy_intp Sx = %(x)s->strides[1]/sizeof(dtype_%(x)s);
npy_intp Sb = %(b)s->strides[0]/sizeof(dtype_%(b)s);
npy_intp Ssm = %(sm)s->strides[1]/sizeof(dtype_%(sm)s);
size_t row_max_j=0;
REAL row_max = x_i[0] + b_i[0];
dtype_%(sm)s row_max = x_i[0] + b_i[0];
// Get the maximum value of the row
for (j = 0; j < Nx[1]; ++j)
{
REAL row_ij = x_i[j * Sx] + b_i[j * Sb];
dtype_%(sm)s row_ij = x_i[j * Sx] + b_i[j * Sb];
// std::cout << "1" << row_ij << "\\n";
row_max_j = (row_ij > row_max) ? j : row_max_j;
row_max = (row_ij > row_max) ? row_ij : row_max;
......@@ -218,9 +217,9 @@ class SoftmaxWithBias(gof.Op):
for (j = 0; j < Nx[1]; ++j)
{
REAL row_ij = x_i[j * Sx] + b_i[j * Sb];
dtype_%(sm)s row_ij = x_i[j * Sx] + b_i[j * Sb];
// std::cout << "2" << row_ij << "\\n";
REAL sm_ij = exp(row_ij - row_max);
dtype_%(sm)s sm_ij = exp(row_ij - row_max);
// std::cout << "3" << sm_ij << "\\n";
sum += sm_ij;
sm_i[j * Ssm] = sm_ij;
......@@ -297,19 +296,13 @@ class SoftmaxGrad(gof.Op):
def c_code_cache_version(self):
return ()
def c_code(self, node, name, (dy, sm), (dx,), sub):
if node.inputs[1].type.dtype != node.inputs[0].type.dtype:
raise NotImplementedError()
if node.inputs[0].type.dtype == 'float32':
REAL = 'float'
else:
REAL = 'double'
return '''
if ((%(dy)s->descr->type_num != PyArray_DOUBLE) && ((%(dy)s->descr->type_num != PyArray_FLOAT)
if ((%(dy)s->descr->type_num != PyArray_DOUBLE) && (%(dy)s->descr->type_num != PyArray_FLOAT))
{
PyErr_SetString(PyExc_TypeError, "types should be float or float64");
%(fail)s;
}
if ((%(sm)s->descr->type_num != PyArray_DOUBLE) && ((%(sm)s->descr->type_num != PyArray_FLOAT)
if ((%(sm)s->descr->type_num != PyArray_DOUBLE) && (%(sm)s->descr->type_num != PyArray_FLOAT))
{
PyErr_SetString(PyExc_TypeError, "types should be float or float64");
%(fail)s;
......@@ -341,12 +334,12 @@ class SoftmaxGrad(gof.Op):
for (size_t i = 0; i < %(dx)s->dimensions[0]; ++i)
{
const REAL* __restrict__ dy_i = (REAL*) (%(dy)s->data + %(dy)s->strides[0] * i);
npy_intp Sdy = %(dy)s->strides[1]/sizeof(REAL);
const REAL* __restrict__ sm_i = (REAL*) (%(sm)s->data + %(sm)s->strides[0] * i);
npy_intp Ssm = %(sm)s->strides[1]/sizeof(REAL);
REAL* __restrict__ dx_i = (REAL*) (%(dx)s->data + %(dx)s->strides[0] * i);
npy_intp Sdx = %(dx)s->strides[1]/sizeof(REAL);
const dtype_%(dy)s* __restrict__ dy_i = (dtype_%(dy)s*) (%(dy)s->data + %(dy)s->strides[0] * i);
npy_intp Sdy = %(dy)s->strides[1]/sizeof(dtype_%(dy)s);
const dtype_%(sm)s* __restrict__ sm_i = (dtype_%(sm)s*) (%(sm)s->data + %(sm)s->strides[0] * i);
npy_intp Ssm = %(sm)s->strides[1]/sizeof(dtype_%(sm)s);
dtype_%(dx)s* __restrict__ dx_i = (dtype_%(dx)s*) (%(dx)s->data + %(dx)s->strides[0] * i);
npy_intp Sdx = %(dx)s->strides[1]/sizeof(dtype_%(dx)s);
double sum_dy_times_sm = 0.;
for (size_t j = 0; j < %(dx)s->dimensions[1]; ++j)
......@@ -506,7 +499,7 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
raise ValueError('y_idx must have same number of rows as x')
sm = numpy.zeros_like(x) # softmax
nll = numpy.zeros(x.shape[0]) #nll(y | softmax(x))
nll = numpy.zeros(x.shape[0], dtype=node.outputs[0].type.dtype) #nll(y | softmax(x))
am = numpy.zeros_like(y_idx)
for i in xrange(sm.shape[0]):
#add the bias vector to the i'th row of x
......@@ -601,7 +594,7 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
begin_row_loop,
"""
const %(y_idx_type)s y_i = ((%(y_idx_type)s*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0];
REAL* __restrict__ nll_i = (REAL*)(%(nll)s->data + %(nll)s->strides[0] * i);
dtype_%(nll)s* __restrict__ nll_i = (dtype_%(nll)s*)(%(nll)s->data + %(nll)s->strides[0] * i);
%(am_type)s* __restrict__ am_i = (%(am_type)s*) (%(am)s->data + %(am)s->strides[0] * i);
""",
inside_row_loop,
......@@ -620,14 +613,12 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
end_row_loop)
def c_code_cache_version(self):
return ()
def c_code(self, node, name, (x, b, y_idx), (nll, sm, am), sub):
y_idx_type = node.inputs[2].type.dtype_specs()[1]
am_type = y_idx_type
code_template = ''.join(self.c_code_template())
if node.inputs[0].type.dtype == 'float32':
REAL = 'float'
else:
REAL = 'double'
return code_template % dict(locals(), **sub)
class CrossentropySoftmax1HotWithBiasDx (gof.Op):
......@@ -654,15 +645,20 @@ class CrossentropySoftmax1HotWithBiasDx (gof.Op):
output_storage[0][0] = dx
def grad(self, *args):
raise NotImplementedError()
def c_code_cache_version(self):
return ()
def c_code(self, node, name, (dnll, sm, y_idx), (dx,), sub):
y_idx_type = node.inputs[2].type.dtype_specs()[1]
return """
if ((%(dnll)s->descr->type_num != PyArray_DOUBLE)
|| (%(sm)s->descr->type_num != PyArray_DOUBLE)
)
if ((%(dnll)s->descr->type_num != PyArray_DOUBLE) && (%(dnll)s->descr->type_num != PyArray_FLOAT))
{
PyErr_SetString(PyExc_TypeError, "dnll type should be float32 or float64");
%(fail)s;
}
if ((%(sm)s->descr->type_num != PyArray_DOUBLE) && (%(sm)s->descr->type_num != PyArray_FLOAT))
{
PyErr_SetString(PyExc_TypeError, "types should be float64, float64, int64");
PyErr_SetString(PyExc_TypeError, "sm type should be float32 or float64");
%(fail)s;
}
if ((%(y_idx)s->descr->type_num != PyArray_INT64)
......@@ -704,15 +700,15 @@ class CrossentropySoftmax1HotWithBiasDx (gof.Op):
for (size_t i = 0; i < %(dx)s->dimensions[0]; ++i)
{
const REAL dnll_i = ((REAL*)(%(dnll)s->data + %(dnll)s->strides[0] * i))[0];
const dtype_%(dnll)s dnll_i = ((dtype_%(dnll)s*)(%(dnll)s->data + %(dnll)s->strides[0] * i))[0];
const %(y_idx_type)s y_i = ((%(y_idx_type)s*)(%(y_idx)s->data + %(y_idx)s->strides[0] * i))[0];
const REAL* __restrict__ sm_i = (REAL*)(%(sm)s->data + %(sm)s->strides[0] * i);
npy_intp Ssm = %(sm)s->strides[1]/sizeof(REAL);
const dtype_%(sm)s* __restrict__ sm_i = (dtype_%(sm)s*)(%(sm)s->data + %(sm)s->strides[0] * i);
npy_intp Ssm = %(sm)s->strides[1]/sizeof(dtype_%(sm)s);
REAL* __restrict__ dx_i = (REAL*)(%(dx)s->data + %(dx)s->strides[0] * i);
npy_intp Sdx = %(dx)s->strides[1]/sizeof(REAL);
dtype_%(dx)s* __restrict__ dx_i = (dtype_%(dx)s*)(%(dx)s->data + %(dx)s->strides[0] * i);
npy_intp Sdx = %(dx)s->strides[1]/sizeof(dtype_%(dx)s);
for (size_t j = 0; j < %(dx)s->dimensions[1]; ++j)
{
......
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