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Fixed over-head bug in the auto-tuner (not in the benchmarks)

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This commit is contained in:
Philippe Tillet
2014-11-06 16:14:46 -05:00
parent 8dd492de23
commit 6595d20c34
9 changed files with 110 additions and 63 deletions
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54
python/autotune/pysrc/autotune.py
View File

@@ -51,9 +51,10 @@ def do_tuning(args):
for operation in ['vector-axpy', 'reduction', 'matrix-axpy', 'row-wise-reduction', 'matrix-product']:
for datatype in [vcl.float32, vcl.float64]:
if not any(x in args.operations for x in [operation, operation + '-' + datatype.__name__]):
if operation not in args.operations and operation + '-' + datatype.__name__ not in args.operations:
continue
ctx = cl.Context([device])
@@ -78,22 +79,25 @@ def do_tuning(args):
def log_uniform_sample(a,b):
return np.exp(np.random.uniform(low=np.log(a), high=np.log(b), size=1)).astype(int)
def log_space_gen_product(a,b,N,dim):
def space_gen_product(a,b,N,dim,method):
N = int(N**(1.0/dim))
def log_space_gen(a,b):
def space_gen(a,b,method):
for i in range(N):
v = int(np.exp(np.log(a) + (np.log(b) - np.log(a))*(i+1)/N))
if method == 'linear':
v = int(a + (b-a)*i/N)
if method == 'log':
v = int(np.exp(np.log(a) + (np.log(b) - np.log(a))*i/N))
yield (v//64 + 1)*64
return tuple(itertools.product(*[log_space_gen(a,b) for i in range(dim)]))
return tuple(itertools.product(*[space_gen(a,b,method) for i in range(dim)]))
#Helper for tuning
def tune(execution_handler, a, b, dimsample, additional_parameters):
def tune(execution_handler, a, b, dimsample, layouts, sample_method_profiles, sample_method_dataset):
print args.build_model
print('-----')
print(' '.join(map(str, ("Now tuning:", datatype.__name__, '-', operation, '-'.join(additional_parameters), '[' + device.name, '(' + device.platform.name + ')]'))))
print(' '.join(map(str, ("Now tuning:", datatype.__name__, '-', operation, '-'.join(layouts), '[' + device.name, '(' + device.platform.name + ')]'))))
#Update JSON
full_operation = operation + ''.join(additional_parameters)
full_operation = operation + ''.join(layouts)
if full_operation not in json_out:
json_out[full_operation] = {}
json_out[full_operation][datatype.__name__] = {}
@@ -105,14 +109,14 @@ def do_tuning(args):
else:
def compute_perf(x, t):
return TYPES[operation]['perf-index']([datatype().itemsize, x, t])
profiles_generator = log_space_gen_product(a, b, args.sample_size, dimsample)
# profiles = dataset.sample_profiles(execution_handler, profiles_generator)
profiles_generator = space_gen_product(a, b, args.sample_size, dimsample, sample_method_profiles)
profiles = dataset.sample_profiles(execution_handler, profiles_generator)
if args.build_model:
dataset_generator = log_space_gen_product(a, b, 1000, dimsample)
# X, Y, profiles = dataset.sample_dataset(os.path.join(full_operation,datatype.__name__), profiles, execution_handler, dataset_generator)
profiles = np.loadtxt('data/vector-axpy/float32/profiles.csv')
X = np.loadtxt('data/vector-axpy/float32/X.csv',ndmin=2)
Y = np.loadtxt('data/vector-axpy/float32/Y.csv',ndmin=2)
dataset_generator = space_gen_product(a, b, 1000, dimsample, sample_method_dataset)
X, Y, profiles = dataset.sample_dataset(os.path.join(full_operation,datatype.__name__), profiles, execution_handler, dataset_generator)
# profiles = np.loadtxt('data/'+full_operation+'/'+datatype.__name__+'/profiles.csv')
# X = np.loadtxt('data/'+full_operation+'/'+datatype.__name__+'/X.csv',ndmin=2)
# Y = np.loadtxt('data/'+full_operation+'/'+datatype.__name__+'/Y.csv',ndmin=2)
clf = train_model(X, Y, profiles, TYPES[operation]['perf-measure'])
D['predictor'] = [{'children_left': e.tree_.children_left.tolist(),
'children_right': e.tree_.children_right.tolist(),
@@ -120,7 +124,7 @@ def do_tuning(args):
'feature': e.tree_.feature.astype('float64').tolist(),
'value': e.tree_.value[:,:,0].astype('float64').tolist()} for e in clf.estimators_]
if args.viennacl_src_path:
misc_tools.update_viennacl_headers(args.viennacl_src_path, device,datatype,operation,additional_parameters,profiles[0])
misc_tools.update_viennacl_headers(args.viennacl_src_path, device,datatype,operation,layouts,profiles[0])
D['profiles'] = [map(int, x) for x in profiles]
@@ -130,7 +134,7 @@ def do_tuning(args):
x = vcl.Vector(sizes[0], context=ctx, dtype=datatype)
y = vcl.Vector(sizes[0], context=ctx, dtype=datatype)
return execute(device, vcl.Assign(y, x + y), (), sizes, fname, parameters)
tune(execution_handler, 1e4, 2e7, 1, ())
tune(execution_handler, 1e3, 2e7, 1, (),'log', 'log')
#Reduction
if operation=='reduction':
def execution_handler(sizes, fname=os.devnull, parameters=None):
@@ -138,14 +142,14 @@ def do_tuning(args):
y = vcl.Vector(sizes[0], context=ctx, dtype=datatype)
s = vcl.Scalar(0, context=ctx, dtype=datatype)
return execute(device, vcl.Assign(s, vcl.Dot(x,y)), (), sizes, fname, parameters)
tune(execution_handler, 1e4, 2e7, 1, ())
tune(execution_handler, 1e3, 2e7, 1, (),'log', 'log')
#Matrix AXPY
if operation=='matrix-axpy':
def execution_handler(sizes, fname=os.devnull, parameters=None):
A = vcl.Matrix(sizes, context=ctx, dtype=datatype, layout=vcl.COL_MAJOR)
C = vcl.Matrix(sizes, context=ctx, dtype=datatype, layout=vcl.COL_MAJOR)
return execute(device, vcl.Assign(C,A + C), (), sizes, fname, parameters)
tune(execution_handler, 100, 4000, 2, ())
tune(execution_handler, 100, 5000, 2, (),'log', 'log')
#Row-wise reduction
if operation=='row-wise-reduction':
for A_trans in args.gemv_layouts:
@@ -155,7 +159,7 @@ def do_tuning(args):
y = vcl.Vector(sizes[0], context=ctx, dtype=datatype)
LHS = A if A_trans=='N' else A.T
return execute(device, vcl.Assign(y, LHS*x), (), sizes, fname, parameters)
tune(execution_handler, 100, 4000, 2, (A_trans,))
tune(execution_handler, 100, 5000, 2, (A_trans,),'log', 'log')
#Matrix Product
if operation=='matrix-product':
for L in args.gemm_layouts:
@@ -170,7 +174,7 @@ def do_tuning(args):
beta = vcl.HostScalar(1.0, context=ctx, dtype = datatype)
C = vcl.Matrix((sizes[0], sizes[1]), context=ctx, dtype = datatype, layout=vcl.COL_MAJOR)
return execute(device, vcl.Assign(C,LHS*RHS*alpha + C*beta),(A_trans,B_trans), sizes, fname, parameters)
tune(execution_handler, 100, 2000, 3,(A_trans,B_trans))
tune(execution_handler, 100, 2000, 3,(A_trans,B_trans), 'linear')
json.dump(json_out, open(args.json_file,'w'))
@@ -227,10 +231,12 @@ class ArgumentsHandler:
args = parser.parse_args()
self.__dict__ = args.__dict__.copy()
#Retypes
self.operations = [self.operations] if not isinstance(self.operations, list) else self.operations
self.device = devices[int(self.device)]
if not self.json_file:
self.json_file = misc_tools.sanitize_string(self.device.name) + '.json'
self.gemm_layouts = self.gemm_layouts.split(',')
self.gemv_layouts = self.gemv_layouts.split(',')
if self.method == 'simple':

13
python/autotune/pysrc/genetic.py
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@@ -132,7 +132,7 @@ class GeneticOperators(object):
tt = misc_tools.benchmark(template, self.statement, self.device)
self.out.write(','.join([str(tt)]+map(str,map(int,parameters)))+'\n')
self.cache[tuple(individual)] = tt
except:
except ValueError:
self.cache[tuple(individual)] = 10
return self.cache[tuple(individual)],
@@ -161,9 +161,14 @@ class GeneticOperators(object):
for _ in xrange(mu):
op_choice = random.random()
if op_choice < cxpb: # Apply crossover
ind1, ind2 = map(self.toolbox.clone, random.sample(population, 2))
ind1, ind2 = self.toolbox.mate(ind1, ind2)
del ind1.fitness.values
while True:
ind1, ind2 = map(self.toolbox.clone, random.sample(population, 2))
ind1, ind2 = self.toolbox.mate(ind1, ind2)
del ind1.fitness.values
parameters = self.decode(ind1)
template = self.build_template(self.TemplateType.Parameters(*parameters))
if not misc_tools.skip(template, self.statement, self.device):
break
offspring.append(ind1)
elif op_choice < cxpb + mutpb: # Apply mutation
ind = self.toolbox.clone(random.choice(population))

8
python/autotune/pysrc/misc_tools.py
View File

@@ -7,6 +7,7 @@ import sys
import pyopencl as cl
import pyviennacl as vcl
import pyatidlas as atd
import numpy as np
class PhysicalLimitsNV:
@@ -214,13 +215,16 @@ def benchmark(template, statement, device):
if occupancy_record.occupancy < 15 :
raise ValueError("Template has too low occupancy")
else:
template.execute(statement, True)
vcl_statements = statements.vcl_tuple
vcl_context = statement.result.context.vcl_sub_context
model = atd._atidlas.model(template._vcl_template, vcl_context, vcl_context.current_device)
model.execute(vcl_statements, False, True)
statement.result.context.finish_all_queues()
current_time = 0
timings = []
while current_time < 1e-1:
time_before = time.time()
template.execute(statement,False)
model.execute(vcl_statements, False, False)
statement.result.context.finish_all_queues()
timings.append(time.time() - time_before)
current_time = current_time + timings[-1]

40
python/autotune/pysrc/model.py
View File

@@ -1,5 +1,6 @@
from sklearn import tree
from sklearn import ensemble
from sklearn.grid_search import GridSearchCV
import numpy as np
def gmean(a, axis=0, dtype=None):
@@ -20,7 +21,6 @@ def nrmse(y_ground, y):
return rmsd/(np.max(y_ground) - np.min(y_ground))
def train_model(X, Y, profiles, metric):
#Shuffle
p = np.random.permutation(X.shape[0])
X = X[p,:]
Y = Y[p,:]
@@ -28,18 +28,34 @@ def train_model(X, Y, profiles, metric):
Ymax = np.max(Y)
Y = Y/Ymax
#Train the model
cut = int(0.9*X.shape[0])
nrmses = {}
for depth in range(1,10):
clf = ensemble.RandomForestRegressor(5, max_depth=4).fit(X[:cut,:], Y[:cut,:])
t = np.argmin(clf.predict(X[cut:,:]), axis = 1)
y = np.array([Y[cut+i,t[i]] for i in range(t.size)])
y_ground = np.min(Y[cut:,:], axis=1)
# for i in range(t.size):
# print X[cut+i,:], y[i], y_ground[i]
nrmses[clf] = nrmse(y_ground, y)
print depth, nrmses[clf]
cut = int(0.95*X.shape[0])
XTr, YTr = X[:cut,:], Y[:cut,:]
XCv, YCv = X[cut:,:], Y[cut:,:]
nrmses = {}
for N in range(1,10):
for depth in range(1,5):
clf = ensemble.RandomForestRegressor(N, max_depth=depth).fit(XTr, YTr)
t = np.argmin(clf.predict(XCv), axis = 1)
y = np.array([YCv[i,t[i]] for i in range(t.size)])
nrmses[clf] = nrmse(np.min(YCv[:,:], axis=1), y)
clf = min(nrmses, key=nrmses.get)
t = np.argmin(clf.predict(XCv), axis = 1)
s = np.array([y[0]/y[k] for y,k in zip(YCv, t)])
tt = np.argmin(YCv, axis = 1)
ss = np.array([y[0]/y[k] for y,k in zip(YCv, tt)])
p5 = lambda a: np.percentile(a, 5)
p25 = lambda a: np.percentile(a, 25)
p50 = lambda a: np.percentile(a, 50)
p75 = lambda a: np.percentile(a, 75)
p95 = lambda a: np.percentile(a, 95)
print("Percentile :\t 5 \t 25 \t 50 \t 75 \t 95")
print("Testing speedup:\t %.2f\t %.2f\t %.2f\t %.2f\t %.3f"%(p5(s), p25(s), p50(s), p75(s), p95(s)))
print("Optimal speedup:\t %.2f\t %.2f\t %.2f\t %.2f\t %.3f"%(p5(ss), p25(ss), p50(ss), p75(ss), p95(ss)))
print clf
return clf

2
python/pyatidlas/CMakeLists.txt
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@@ -2,7 +2,7 @@ set(SETUP_PY_IN "${CMAKE_CURRENT_SOURCE_DIR}/setup.py")
set(SETUP_PY "${CMAKE_CURRENT_BINARY_DIR}/setup.py")
set(OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/build/timestamp")
file(GLOB DEPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "${CMAKE_CURRENT_SOURCE_DIR}/pyatidlas/*.py ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp")
list(APPEND DEPS "${CMAKE_CURRENT_SOURCE_DIR}/setup.py")
list(APPEND DEPS "${CMAKE_CURRENT_SOURCE_DIR}/setup.py" "${CMAKE_CURRENT_SOURCE_DIR}/src/_atidlas.cpp" "${CMAKE_CURRENT_SOURCE_DIR}/pyatidlas/pycore.py")
configure_file(${SETUP_PY_IN} ${SETUP_PY})
add_custom_command(OUTPUT ${OUTPUT}

6
python/pyatidlas/setup.py
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@@ -44,10 +44,10 @@ def main():
DEFINES = [('VIENNACL_WITH_OPENCL',None), ('VIENNACL_WITH_OPENMP', None),
('boost','pyviennaclboost')]
INCLUDE_DIRS = ['/home/philippe/Development/pyviennacl-dev/external/boost-python-ublas-subset/boost_subset/',
INCLUDE_DIRS = ['${CMAKE_CURRENT_SOURCE_DIR}/external/pyviennacl-dev/external/boost-python-ublas-subset/boost_subset/',
'${PROJECT_SOURCE_DIR}',
'/home/philippe/Development/pyviennacl-dev/external/viennacl-dev']
LIBRARY_DIRS = ['/home/philippe/Development/pyviennacl-dev/build/lib.linux-x86_64-2.7/pyviennacl/']
'${CMAKE_CURRENT_SOURCE_DIR}/external/pyviennacl-dev/external/viennacl-dev']
LIBRARY_DIRS = ['${CMAKE_CURRENT_SOURCE_DIR}/external/pyviennacl-dev/build/lib.linux-x86_64-2.7/pyviennacl/']
setup(
name="pyatidlas",

10
python/pyatidlas/src/_atidlas.cpp
View File

@@ -10,18 +10,22 @@
#include "atidlas/templates/row_wise_reduction.hpp"
#include "atidlas/templates/matrix_product.hpp"
#include "atidlas/execute.hpp"
#include "atidlas/model/model.hpp"
#define ENUM_VALUE(NS, V) .value( #V, NS :: V )
namespace bp = boost::python;
namespace vcl = viennacl;
namespace atd = atidlas;
void export_atidlas()
{
bp::def("execute", &atidlas::execute);
bp::class_<atidlas::model>("model", bp::init<atd::template_base const &, vcl::ocl::context &, vcl::ocl::device const & >())
.def("execute", &atd::model::execute)
;
bp::enum_<atidlas::fetching_policy_type>
("fetching_policy_type")
ENUM_VALUE(atidlas, FETCH_FROM_LOCAL)
@@ -107,4 +111,4 @@ BOOST_PYTHON_MODULE(_atidlas)
bp::object package = bp::scope();
package.attr("__path__") = "_atidlas";
export_atidlas();
}
}