def gemm_cpu_prepare_vectorized_FC(alpha, beta, M, N, K, dtype, Astrides,
Bstrides, Cstrides, MB, NB, KB):
ctype = ctype_from_dtype(dtype)
(As0, As1) = elemstrides(Astrides, dtype, MB, vecdim=0)
(Bs0, Bs1) = elemstrides(Bstrides, dtype, NB, vecdim=1)
(Cs0, Cs1) = elemstrides(Cstrides, dtype, NB, vecdim=1)
NoMB = M // MB
NoNB = N // NB
NoKB = K // KB
if M != MB * NoMB:
raise BlockingError()
if N != NB * NoNB:
raise BlockingError()
if K != KB * NoKB:
raise BlockingError()
if NB > 16:
raise BlockingError('codegen breaks at this point')
text = Template(vectorized_text_FC,
output_encoding='ascii').render(**locals())
if 0:
for ii, line in enumerate(text.split('\n')):
print ii, line
prg = cl.Program(ctx, text).build()
#print 'built!'
return prg.kern
def pairwise_cpu_prepare_vectorized_CF(alpha, beta, M, N, K, dtype, Astrides, Bstrides, Cstrides, MB, NB, KB):
ctype = ctype_from_dtype(dtype)
(As0, As1) = elemstrides(Astrides, dtype, KB, vecdim=1)
(Bs0, Bs1) = elemstrides(Bstrides, dtype, KB, vecdim=0)
(Cs0, Cs1) = elemstrides(Cstrides, dtype, NB, vecdim=1)
NoMB = M // MB
NoNB = N // NB
NoKB = K // KB
if M != MB * NoMB:
raise BlockingError()
if N != NB * NoNB:
raise BlockingError()
if K != KB * NoKB:
raise BlockingError()
if NB > 16:
raise BlockingError("codegen breaks at this point")
text = Template(vectorized_text_CF, output_encoding="ascii").render(**locals())
if 0:
for ii, line in enumerate(text.split("\n")):
print ii, line
prg = cl.Program(ctx, text).build()
# print 'built!'
return prg.kern
def _plan_template(queue, name, core_text, declares="", tag=None, n_elements=0, inputs={}, outputs={}, parameters={}):
"""Template for making a plan for vector nonlinearities.
This template assumes that all inputs and outputs are vectors.
Parameters
----------
n_elements: int
If n_elements == 0, then the kernels are allocated as a block. This is
simple, but can be slow for large computations where input vector sizes
are not uniform (e.g. one large population and many small ones).
If n_elements >= 1, then all the vectors in the RaggedArray are
flattened so that the exact number of required kernels is allocated.
Each kernel performs computations for `n_elements` elements.
inputs: dictionary of CLRaggedArrays
Inputs to the function. RaggedArrays must be a list of vectors.
outputs: dictionary of CLRaggedArrays
Outputs of the function. RaggedArrays must be a list of vectors.
parameters: dictionary of CLRaggedArrays
Parameters to the function. Each RaggedArray element must be a vector
of the same length of the inputs, or a scalar (to be broadcasted).
Providing a float instead of a RaggedArray makes that parameter
constant.
"""
base = inputs.values()[0] # input to use as reference (for lengths)
N = len(base)
### split parameters into static and updated params
static_params = {} # static params (hard-coded)
params = {} # variable params (updated)
for k, v in parameters.items():
if isinstance(v, CLRaggedArray):
params[k] = v
else:
try:
static_params[k] = ("float", float(v))
except TypeError:
raise
avars = {}
for vname, v in inputs.items() + outputs.items():
assert vname not in avars, "Name clash"
assert len(v) == N
assert all_equal(v.shape0s, base.shape0s)
### N.B. - we should be able to ignore ldas as long as all vectors
assert all_equal(v.shape1s, 1)
dtype = v.cl_buf.ocldtype
offset = "%(name)s_starts[n]" % {"name": vname}
avars[vname] = (dtype, offset)
for vname, v in params.items():
assert vname not in avars, "Name clash"
assert len(v) == N
for i in xrange(N):
assert v.shape0s[i] == base.shape0s[i] or v.shape0s[i] == 1, "%s.shape0s[%d] must be 1 or %d (not %d)" % (
vname,
i,
base.shape0s[i],
v.shape0s[i],
)
assert v.shape1s[i] == 1
dtype = v.cl_buf.ocldtype
offset = "%(name)s_starts[n]" % {"name": vname}
avars[vname] = (dtype, offset)
ivars = dict((k, avars[k]) for k in inputs.keys())
ovars = dict((k, avars[k]) for k in outputs.keys())
pvars = dict((k, avars[k]) for k in params.keys())
textconf = dict(
N=N,
n_elements=n_elements,
tag=str(tag),
declares=declares,
core_text=core_text,
ivars=ivars,
ovars=ovars,
pvars=pvars,
static_params=static_params,
)
if n_elements > 0:
### Allocate the exact number of required kernels in a vector
gsize = (int(np.ceil(np.sum(base.shape0s) / float(n_elements))),)
text = """
////////// MAIN FUNCTION //////////
__kernel void fn(
% for name, [type, offset] in ivars.items():
__global const int *${name}_starts,
__global const ${type} *in_${name},
% endfor
% for name, [type, offset] in ovars.items():
__global const int *${name}_starts,
__global ${type} *in_${name},
% endfor
% for name, [type, offset] in pvars.items():
__global const int *${name}_starts,
__global const int *${name}_shape0s,
__global const ${type} *in_${name},
% endfor
__global const int *lengths
)
{
const int gid = get_global_id(0);
int m = gid * ${n_elements}, n = 0;
while (m >= lengths[n]) {
m -= lengths[n];
n++;
}
if (n >= ${N}) return;
% for name, [type, offset] in ivars.items():
__global const ${type} *cur_${name} = in_${name} + ${offset} + m;
% endfor
% for name, [type, offset] in ovars.items():
__global ${type} *cur_${name} = in_${name} + ${offset} + m;
% endfor
% for name, [type, offset] in pvars.items():
__global const ${type} *cur_${name} = in_${name} + ${offset};
int ${name}_isvector = ${name}_shape0s[n] > 1;
if (${name}_isvector) cur_${name} += m;
% endfor
% for name, [type, offset] in ivars.items() + ovars.items() + pvars.items():
${type} ${name};
% endfor
% for name, [type, value] in static_params.items():
const ${type} ${name} = ${value};
% endfor
//////////////////////////////////////////////////
//vvvvv USER DECLARATIONS BELOW vvvvv
${declares}
//^^^^^ USER DECLARATIONS ABOVE ^^^^^
//////////////////////////////////////////////////
% for ii in range(n_elements):
//////////////////////////////////////////////////
////////// LOOP ITERATION ${ii}
% for name, [type, offset] in ivars.items():
${name} = *cur_${name};
% endfor
% for name, [type, offset] in pvars.items():
if ((${ii} == 0) || ${name}_isvector) ${name} = *cur_${name};
% endfor
/////vvvvv USER COMPUTATIONS BELOW vvvvv
${core_text}
/////^^^^^ USER COMPUTATIONS ABOVE ^^^^^
% for name, [type, offset] in ovars.items():
*cur_${name} = ${name};
% endfor
% if ii + 1 < n_elements:
m++;
if (m >= lengths[n]) {
n++;
m = 0;
if (n >= ${N}) return;
% for name, [type, offset] in ivars.items() + ovars.items() + pvars.items():
cur_${name} = in_${name} + ${offset};
% endfor
% for name, [type, offset] in pvars.items():
${name}_isvector = ${name}_shape0s[n] > 1;
if (!${name}_isvector) ${name} = *cur_${name};
% endfor
} else {
% for name, [type, offset] in ivars.items() + ovars.items():
cur_${name}++;
% endfor
% for name, [type, offset] in pvars.items():
if (${name}_isvector) cur_${name}++;
% endfor
}
% endif
% endfor
}
"""
else:
### Allocate more than enough kernels in a matrix
gsize = (int(np.max(base.shape0s)), int(N))
text = """
////////// MAIN FUNCTION //////////
__kernel void fn(
% for name, [type, offset] in ivars.items():
__global const int *${name}_starts,
__global const ${type} *in_${name},
% endfor
% for name, [type, offset] in ovars.items():
__global const int *${name}_starts,
__global ${type} *in_${name},
% endfor
% for name, [type, offset] in pvars.items():
__global const int *${name}_starts,
__global const int *${name}_shape0s,
__global const ${type} *in_${name},
% endfor
__global const int *lengths
)
{
const int m = get_global_id(0);
const int n = get_global_id(1);
const int M = lengths[n];
if (m >= M) return;
% for name, [type, offset] in ivars.items():
${type} ${name} = in_${name}[${offset} + m];
% endfor
% for name, [type, offset] in ovars.items():
${type} ${name};
% endfor
% for name, [type, offset] in pvars.items():
const ${type} ${name} = (${name}_shape0s[n] > 1) ?
in_${name}[${offset} + m] : in_${name}[${offset}];
% endfor
% for name, [type, value] in static_params.items():
const ${type} ${name} = ${value};
% endfor
//////////////////////////////////////////////////
//vvvvv USER DECLARATIONS BELOW vvvvv
${declares}
//^^^^^ USER DECLARATIONS ABOVE ^^^^^
//////////////////////////////////////////////////
/////vvvvv USER COMPUTATIONS BELOW vvvvv
${core_text}
/////^^^^^ USER COMPUTATIONS ABOVE ^^^^^
% for name, [type, offset] in ovars.items():
in_${name}[${offset} + m] = ${name};
% endfor
}
"""
text = Template(text, output_encoding="ascii").render(**textconf)
if 0:
for i, line in enumerate(text.split("\n")):
print "%3d %s" % (i + 1, line)
full_args = []
for name, v in inputs.items() + outputs.items():
full_args.extend([v.cl_starts, v.cl_buf])
for name, v in params.items():
full_args.extend([v.cl_starts, v.cl_shape0s, v.cl_buf])
full_args.append(base.cl_shape0s)
full_args = tuple(full_args)
_fn = cl.Program(queue.context, text).build().fn
_fn.set_args(*[arr.data for arr in full_args])
rval = Plan(queue, _fn, gsize, lsize=None, name=name, tag=tag)
rval.full_args = full_args # prevent garbage-collection
return rval
def generate(self, filename=None):
formatted_data = np.ndarray(shape=self.data.shape,
dtype=np.dtype('U25'))
rule = np.ndarray(shape=self.data.shape[0], dtype=np.dtype('U25'))
for i in range(self.data.shape[0]):
for j in range(self.data.shape[1]):
formatted_data[i, j] = self.format(i, j)
rule[i] = self.hrule_adder(i)
result_template = r"""\documentclass[11pt]{article}
\usepackage{multirow}
\usepackage{booktabs}
${preamble}
\thispagestyle{empty}
\begin{document}
\begin{table}
\begin{tabular}{${'c'*(data.shape[1]+len(row_names))}}
\toprule
% for col_name in col_names:
<%
cols_per_cell = data.shape[1] // len(col_name)
alignment = 'c'
delimiter = r'} & \multicolumn{' + str(cols_per_cell) + r'}{' + alignment +r'}{' # noqa
%>
\multicolumn{${len(row_names)}}{c}{} ${delimiter[2:]}${delimiter.join(col_name)}}\\\
% endfor
\midrule
% for i in range(data.shape[0]):
% for row_name in row_names:
<% nrows_per_row_name = data.shape[0] // len(row_name) %>
${r'\multirow{%d}{*}{%s}' % (nrows_per_row_name,
row_name[i//nrows_per_row_name]) if i % nrows_per_row_name == 0 else ' '} & \
% endfor
${' & '.join(elem for elem in data[i, :])}\\\
${rule[i]}
% endfor
\bottomrule
\end{tabular}
\end{table}
\end{document}
"""
result = Template(result_template).render(row_names=self.row_names,
col_names=self.column_names,
data=formatted_data,
preamble=self.preamble(),
rule=rule)
if filename is None:
print(result)
return
ext = filename[-4:]
if ext == ".tex":
# if the desired output is tex only, then only extract the tabular
# part.
resultlines = result.split("\n")
begintabular_idx, = [
i for i, line in enumerate(resultlines)
if line.startswith(r"\begin{tabular}")
]
endtabular_idx, = [
i for i, line in enumerate(resultlines)
if line.startswith(r"\end{tabular}")
]
result = "\n".join(resultlines[begintabular_idx:endtabular_idx +
1])
with open(filename, 'w') as f:
f.write(result)
elif ext == ".pdf" or ext == ".png":
tex_filename = (
'/tmp/' +
''.join(random.choice(string.ascii_letters)
for _ in range(9)) + '.tex')
gen_pdf_filename = tex_filename[:-4] + '.pdf'
with open(tex_filename, 'w') as f:
f.write(result)
subprocess.call(
('pdflatex -output-directory=/tmp {0}'.format(tex_filename)
).split())
if ext == ".pdf":
subprocess.call(('mv {0} {1}'.format(gen_pdf_filename,
filename)).split())
if ext == ".png":
cropped_pdf = gen_pdf_filename[:-4] + '-crop.pdf'
subprocess.call(('pdfcrop -margin 3 {0} {1}'.format(
gen_pdf_filename, cropped_pdf)).split())
with open(filename, 'w') as f:
ppm = subprocess.Popen(
('pdftoppm -r 500 {0}'.format(cropped_pdf)).split(),
stdout=subprocess.PIPE)
subprocess.call(['pnmtopng'], stdin=ppm.stdout, stdout=f)
ppm.wait()
else:
raise NotImplementedError()
def _plan_template(queue,
name,
core_text,
declares="",
tag=None,
n_elements=0,
inputs={},
outputs={},
parameters={}):
"""Template for making a plan for vector nonlinearities.
This template assumes that all inputs and outputs are vectors.
Parameters
----------
n_elements: int
If n_elements == 0, then the kernels are allocated as a block. This is
simple, but can be slow for large computations where input vector sizes
are not uniform (e.g. one large population and many small ones).
If n_elements >= 1, then all the vectors in the RaggedArray are
flattened so that the exact number of required kernels is allocated.
Each kernel performs computations for `n_elements` elements.
inputs: dictionary of CLRaggedArrays
Inputs to the function. RaggedArrays must be a list of vectors.
outputs: dictionary of CLRaggedArrays
Outputs of the function. RaggedArrays must be a list of vectors.
parameters: dictionary of CLRaggedArrays
Parameters to the function. Each RaggedArray element must be a vector
of the same length of the inputs, or a scalar (to be broadcasted).
Providing a float instead of a RaggedArray makes that parameter
constant.
"""
base = inputs.values()[0] # input to use as reference (for lengths)
N = len(base)
### split parameters into static and updated params
static_params = {} # static params (hard-coded)
params = {} # variable params (updated)
for k, v in parameters.items():
if isinstance(v, CLRaggedArray):
params[k] = v
else:
try:
static_params[k] = ('float', float(v))
except TypeError:
raise
avars = {}
for vname, v in inputs.items() + outputs.items():
assert vname not in avars, "Name clash"
assert len(v) == N
assert all_equal(v.shape0s, base.shape0s)
### N.B. - we should be able to ignore ldas as long as all vectors
assert all_equal(v.shape1s, 1)
dtype = v.cl_buf.ocldtype
offset = '%(name)s_starts[n]' % {'name': vname}
avars[vname] = (dtype, offset)
for vname, v in params.items():
assert vname not in avars, "Name clash"
assert len(v) == N
for i in xrange(N):
assert v.shape0s[i] == base.shape0s[i] or v.shape0s[i] == 1, \
"%s.shape0s[%d] must be 1 or %d (not %d)" % \
(vname, i, base.shape0s[i], v.shape0s[i])
assert v.shape1s[i] == 1
dtype = v.cl_buf.ocldtype
offset = '%(name)s_starts[n]' % {'name': vname}
avars[vname] = (dtype, offset)
ivars = dict((k, avars[k]) for k in inputs.keys())
ovars = dict((k, avars[k]) for k in outputs.keys())
pvars = dict((k, avars[k]) for k in params.keys())
textconf = dict(N=N,
n_elements=n_elements,
tag=str(tag),
declares=declares,
core_text=core_text,
ivars=ivars,
ovars=ovars,
pvars=pvars,
static_params=static_params)
if n_elements > 0:
### Allocate the exact number of required kernels in a vector
gsize = (int(np.ceil(np.sum(base.shape0s) / float(n_elements))), )
text = """
////////// MAIN FUNCTION //////////
__kernel void fn(
% for name, [type, offset] in ivars.items():
__global const int *${name}_starts,
__global const ${type} *in_${name},
% endfor
% for name, [type, offset] in ovars.items():
__global const int *${name}_starts,
__global ${type} *in_${name},
% endfor
% for name, [type, offset] in pvars.items():
__global const int *${name}_starts,
__global const int *${name}_shape0s,
__global const ${type} *in_${name},
% endfor
__global const int *lengths
)
{
const int gid = get_global_id(0);
int m = gid * ${n_elements}, n = 0;
while (m >= lengths[n]) {
m -= lengths[n];
n++;
}
if (n >= ${N}) return;
% for name, [type, offset] in ivars.items():
__global const ${type} *cur_${name} = in_${name} + ${offset} + m;
% endfor
% for name, [type, offset] in ovars.items():
__global ${type} *cur_${name} = in_${name} + ${offset} + m;
% endfor
% for name, [type, offset] in pvars.items():
__global const ${type} *cur_${name} = in_${name} + ${offset};
int ${name}_isvector = ${name}_shape0s[n] > 1;
if (${name}_isvector) cur_${name} += m;
% endfor
% for name, [type, offset] in ivars.items() + ovars.items() + pvars.items():
${type} ${name};
% endfor
% for name, [type, value] in static_params.items():
const ${type} ${name} = ${value};
% endfor
//////////////////////////////////////////////////
//vvvvv USER DECLARATIONS BELOW vvvvv
${declares}
//^^^^^ USER DECLARATIONS ABOVE ^^^^^
//////////////////////////////////////////////////
% for ii in range(n_elements):
//////////////////////////////////////////////////
////////// LOOP ITERATION ${ii}
% for name, [type, offset] in ivars.items():
${name} = *cur_${name};
% endfor
% for name, [type, offset] in pvars.items():
if ((${ii} == 0) || ${name}_isvector) ${name} = *cur_${name};
% endfor
/////vvvvv USER COMPUTATIONS BELOW vvvvv
${core_text}
/////^^^^^ USER COMPUTATIONS ABOVE ^^^^^
% for name, [type, offset] in ovars.items():
*cur_${name} = ${name};
% endfor
% if ii + 1 < n_elements:
m++;
if (m >= lengths[n]) {
n++;
m = 0;
if (n >= ${N}) return;
% for name, [type, offset] in ivars.items() + ovars.items() + pvars.items():
cur_${name} = in_${name} + ${offset};
% endfor
% for name, [type, offset] in pvars.items():
${name}_isvector = ${name}_shape0s[n] > 1;
if (!${name}_isvector) ${name} = *cur_${name};
% endfor
} else {
% for name, [type, offset] in ivars.items() + ovars.items():
cur_${name}++;
% endfor
% for name, [type, offset] in pvars.items():
if (${name}_isvector) cur_${name}++;
% endfor
}
% endif
% endfor
}
"""
else:
### Allocate more than enough kernels in a matrix
gsize = (int(np.max(base.shape0s)), int(N))
text = """
////////// MAIN FUNCTION //////////
__kernel void fn(
% for name, [type, offset] in ivars.items():
__global const int *${name}_starts,
__global const ${type} *in_${name},
% endfor
% for name, [type, offset] in ovars.items():
__global const int *${name}_starts,
__global ${type} *in_${name},
% endfor
% for name, [type, offset] in pvars.items():
__global const int *${name}_starts,
__global const int *${name}_shape0s,
__global const ${type} *in_${name},
% endfor
__global const int *lengths
)
{
const int m = get_global_id(0);
const int n = get_global_id(1);
const int M = lengths[n];
if (m >= M) return;
% for name, [type, offset] in ivars.items():
${type} ${name} = in_${name}[${offset} + m];
% endfor
% for name, [type, offset] in ovars.items():
${type} ${name};
% endfor
% for name, [type, offset] in pvars.items():
const ${type} ${name} = (${name}_shape0s[n] > 1) ?
in_${name}[${offset} + m] : in_${name}[${offset}];
% endfor
% for name, [type, value] in static_params.items():
const ${type} ${name} = ${value};
% endfor
//////////////////////////////////////////////////
//vvvvv USER DECLARATIONS BELOW vvvvv
${declares}
//^^^^^ USER DECLARATIONS ABOVE ^^^^^
//////////////////////////////////////////////////
/////vvvvv USER COMPUTATIONS BELOW vvvvv
${core_text}
/////^^^^^ USER COMPUTATIONS ABOVE ^^^^^
% for name, [type, offset] in ovars.items():
in_${name}[${offset} + m] = ${name};
% endfor
}
"""
text = Template(text, output_encoding='ascii').render(**textconf)
if 0:
for i, line in enumerate(text.split('\n')):
print "%3d %s" % (i + 1, line)
full_args = []
for vname, v in inputs.items() + outputs.items():
full_args.extend([v.cl_starts, v.cl_buf])
for vname, v in params.items():
full_args.extend([v.cl_starts, v.cl_shape0s, v.cl_buf])
full_args.append(base.cl_shape0s)
full_args = tuple(full_args)
_fn = cl.Program(queue.context, text).build().fn
_fn.set_args(*[arr.data for arr in full_args])
rval = Plan(queue, _fn, gsize, lsize=None, name=name, tag=tag)
rval.full_args = full_args # prevent garbage-collection
return rval