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Input-dependent models now activated for all the operations

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This commit is contained in:
Philippe Tillet
2014-10-04 08:58:11 +02:00
parent 044419f9f0
commit fc8b450a7c
5 changed files with 89 additions and 90 deletions
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42
autotune/python/autotune.py
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@@ -8,6 +8,7 @@ from external.configobj import ConfigObj
import pyopencl as cl
import pyviennacl as vcl
import numpy as np
from pyviennacl import backend
from pyviennacl import opencl
from pyviennacl import atidlas
@@ -73,32 +74,45 @@ def do_tuning(config_fname, spec_fname, viennacl_root):
with open(fname, "w+") as archive:
return optimize.genetic(statement, device, TYPES[operation]['template'], lambda p: TYPES[operation]['template'](p, *other_params),
lambda t: TYPES[operation]['perf-index']([datatype().itemsize, sizes, t]), TYPES[operation]['perf-measure'], archive)
#Helper
def tune(execution_handler, nTuning, nDataPoints, draw):
if 'size' in p:
profile = execution_handler(map_to_list(int, p['size']))
else:
def compute_perf(x, t):
return TYPES[operation]['perf-index']([datatype().itemsize, x, t])
X, Y, profiles = generate_dataset(TYPES[operation]['template'], execution_handler, nTuning, nDataPoints, compute_perf, draw)
train_model(X, Y, profiles, TYPES[operation]['perf-measure'])
#Vector AXPY
if operation=='vector-axpy':
def execution_handler(sizes, fname=os.devnull, parameters=None):
x = vcl.Vector(sizes[0], context=ctx, dtype=datatype)
y = vcl.Vector(sizes[0], context=ctx, dtype=datatype)
return execute(device, vcl.Statement(vcl.ElementProd(vcl.exp(x + y),vcl.cos(x + y))), (), sizes, fname, parameters)
if 'size' in p:
profile = execution_handler(map_to_list(int, p['size']))
tune(execution_handler, 50, 10000, lambda : 64*np.random.randint(low=10, high=100000, size=1))
#Matrix AXPY
if operation=='matrix-axpy':
A = vcl.Matrix(s, context=ctx, dtype=datatype)
B = vcl.Matrix(s, context=ctx, dtype=datatype)
execute(A+B, ())
def execution_handler(sizes, fname=os.devnull, parameters=None):
A = vcl.Matrix(sizes, context=ctx, dtype=datatype)
B = vcl.Matrix(sizes, context=ctx, dtype=datatype)
return execute(device, vcl.Statement(A+B), (), sizes, fname, parameters)
tune(execution_handler, 50, 10000, lambda : 64*np.random.randint(low=5, high=100, size=2))
#Row-wise reduction
if operation=='row-wise-reduction':
layouts = map_to_list((str,p['layout']))
layouts = map_to_list(str,p['layout'])
if 'all' in layouts:
layouts = ['N', 'T']
for A_trans in layouts:
A = vcl.Matrix(s if A_trans=='N' else s[::-1], context=ctx, dtype=datatype, layout=vcl.COL_MAJOR)
x = vcl.Vector(s[1] if A_trans=='N' else s[0], context=ctx, dtype=datatype)
LHS = A if A_trans=='N' else A.T
execute(LHS*x, ())
def execution_handler(sizes, fname=os.devnull, parameters=None):
A = vcl.Matrix(sizes if A_trans=='N' else sizes[::-1], context=ctx, dtype=datatype, layout=vcl.COL_MAJOR)
x = vcl.Vector(sizes[1] if A_trans=='N' else sizes[0], context=ctx, dtype=datatype)
LHS = A if A_trans=='N' else A.T
execute(device, vcl.Statement(LHS*x), (), sizes, fname, parameters)
tune(execution_handler, 50, 10000, lambda : 64*np.random.randint(low=5, high=100, size=2))
#Matrix Product
if operation=='matrix-product':
layouts = map_to_list((str,p['layout']))
layouts = map_to_list(str,p['layout'])
if 'all' in layouts:
layouts = ['NN', 'NT', 'TN', 'TT']
for layout in layouts:
@@ -114,11 +128,7 @@ def do_tuning(config_fname, spec_fname, viennacl_root):
C = vcl.Matrix((sizes[0], sizes[2]), context=ctx, dtype = datatype, layout=vcl.COL_MAJOR)
statement = vcl.Statement(vcl.Assign(C,LHS*RHS*alpha + C*beta))
return execute(device, statement,(A_trans, B_trans), sizes, fname, parameters)
if 'size' in p:
profile = execution_handler(map(int, p['size']))
else:
X, Y, profiles = generate_dataset(TYPES[operation]['template'], execution_handler)
train_model(X, Y, profiles)
tune(execution_handler, 50, 10000, lambda : 64*np.random.randint(low=1, high=40, size=3))