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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
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@@ -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':