from __future__ import division import argparse, itertools, os, sys, json import misc_tools, optimize, dataset import pyopencl as cl import pyviennacl as vcl import pyatidlas as atd import numpy as np from numpy import random from model import train_model TYPES = { 'vector-axpy': {'template':atd.VectorAxpyTemplate, 'perf-index':lambda x: 2*x[0]*x[1][0]/x[2]*1e-9, 'perf-measure':'GB/s'}, 'matrix-axpy': {'template':atd.MatrixAxpyTemplate, 'perf-index':lambda x: 2*x[0]*x[1][0]*x[1][1]/x[2]*1e-9, 'perf-measure':'GB/s'}, 'reduction': {'template':atd.ReductionTemplate, 'perf-index':lambda x: 2*x[0]*x[1][0]/x[2]*1e-9, 'perf-measure':'GB/s'}, 'row-wise-reduction': {'template':atd.RowWiseReductionTemplate, 'perf-index':lambda x: x[0]*x[1][0]*x[1][1]/x[2]*1e-9, 'perf-measure':'GB/s'}, 'matrix-product': {'template': atd.MatrixProductTemplate, 'perf-index': lambda x: 2*x[1][0]*x[1][1]*x[1][2]/x[2]*1e-9, 'perf-measure': 'GFLOP/s'} } def do_tuning(args, devices): device = devices[args.device] dname = misc_tools.sanitize_string(device.name) json_out = {} json_out["version"] = "1.0" def map_to_list(T, x): return list(map(T, x if isinstance(x, list) else [x])) if(args.method=='simple'): default_tuning_sizes = {'vector-axpy': [args.blas1_size], 'reduction': [args.blas1_size], 'matrix-axpy' : args.blas2_size, 'row-wise-reduction' : args.blas2_size, 'matrix-product': args.blas3_size} for operation in ['vector-axpy', 'reduction', 'matrix-axpy', 'row-wise-reduction', 'matrix-product']: for datatype in [vcl.float32, vcl.float64]: if any(x in args.exclude_operations.split(',') for x in [operation, operation + '-' + datatype.__name__]): continue ctx = cl.Context([device]) ctx = vcl.backend.Context(ctx) #Check data-type if datatype is vcl.float64 and not device.double_fp_config: sys.stderr.write('Warning : The device ' + device.name + ' does not support double precision! Skipping ...') continue #Helper for execution def execute(device, node, other_params, sizes, fname = os.devnull, parameters = None): with vcl.Statement(node) as statement: if parameters is not None: TemplateType = TYPES[operation]['template'] return misc_tools.benchmark(TemplateType(TemplateType.Parameters(*parameters),*other_params), statement, device) 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 for tuning def tune(execution_handler, profiles_generator, dataset_generator, additional_parameters): print('-----') print(' '.join(map(str, ("Now tuning:", datatype.__name__, '-', operation, '-'.join(additional_parameters), '[' + device.name, '(' + device.platform.name + ')]')))) #Update JSON full_operation = operation + ''.join(additional_parameters) if full_operation not in json_out: json_out[full_operation] = {} json_out[full_operation][datatype.__name__] = {} D = json_out[full_operation][datatype.__name__] if args.method == 'simple': print default_tuning_sizes[operation] profiles = [execution_handler(map(int,default_tuning_sizes[operation]))] else: def compute_perf(x, t): return TYPES[operation]['perf-index']([datatype().itemsize, x, t]) profiles = dataset.sample_profiles(execution_handler, profiles_generator) if args.build_model: X, Y, profiles = dataset.sample_dataset(os.path.join(full_operation,datatype.__name__), profiles, execution_handler, dataset_generator) 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(), 'threshold': e.tree_.threshold.astype('float32').tolist(), 'feature': e.tree_.feature.astype('float32').tolist(), 'value': e.tree_.value[:,:,0].astype('float32').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]) D['profiles'] = [map(int, x) for x in profiles] 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): N = int(N**(1.0/dim)) def log_space_gen(a,b): for i in range(N): v = int(np.exp(np.log(a) + (np.log(b) - np.log(a))*(i+1)/N)) yield (v//64 + 1)*64 return tuple(itertools.product(*[log_space_gen(a,b) for i in range(dim)])) #Vector AXPY if operation=='vector-axpy': def execution_handler(sizes, fname=os.devnull, parameters=None): x = vcl.Vector(sizes[0], context=ctx, dtype=datatype) z = vcl.Vector(sizes[0], context=ctx, dtype=datatype) return execute(device, vcl.Assign(z, x), (), sizes, fname, parameters) tune(execution_handler, log_space_gen_product(1e3, 1e7, args.sample_size, 1), log_space_gen_product(1e3, 1e7, 1000, 1), ()) #Reduction if operation=='reduction': 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) s = vcl.Scalar(0, context=ctx, dtype=datatype) return execute(device, vcl.Assign(s, vcl.Dot(x,y)), (), sizes, fname, parameters) tune(execution_handler, log_space_gen_product(1e3, 1e7, args.sample_size, 1), log_space_gen_product(1e3, 1e7, 1000, 1), ()) #Matrix AXPY if operation=='matrix-axpy': def execution_handler(sizes, fname=os.devnull, parameters=None): A = vcl.Matrix(sizes, context=ctx, dtype=datatype) C = vcl.Matrix(sizes, context=ctx, dtype=datatype) return execute(device, vcl.Assign(C,A), (), sizes, fname, parameters) tune(execution_handler, log_space_gen_product(100, 4000, args.sample_size, 2), log_space_gen_product(100, 4000, 1000, 2), ()) #Row-wise reduction if operation=='row-wise-reduction': for A_trans in args.gemv_layouts.split(','): 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], context=ctx, dtype=datatype) 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, log_space_gen_product(100, 4000, args.sample_size, 2), log_space_gen_product(100, 4000, 1000, 2), (A_trans,)) #Matrix Product if operation=='matrix-product': for layout in args.gemm_layouts.split(','): def execution_handler(sizes, fname=os.devnull, parameters=None): A_trans = layout[0] B_trans = layout[1] A = vcl.Matrix((sizes[0], sizes[1]) if A_trans=='N' else (sizes[1],sizes[0]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR); B = vcl.Matrix((sizes[1], sizes[2]) if B_trans=='N' else (sizes[2],sizes[1]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR); LHS = A if A_trans=='N' else A.T RHS = B if B_trans=='N' else B.T alpha = vcl.HostScalar(1.0, context=ctx, dtype = datatype) beta = vcl.HostScalar(1.0, context=ctx, dtype = datatype) C = vcl.Matrix((sizes[0], sizes[2]), 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, log_space_gen_product(100, 4000, args.sample_size, 3), log_space_gen_product(100, 4000, 1000, 3),(layout[0], layout[1])) json.dump(json_out, open(dname + '.json','w')) if __name__ == "__main__": parser = argparse.ArgumentParser() subparsers = parser.add_subparsers(dest='action') print_devices_parser = subparsers.add_parser('list-devices', help='list the devices available') tune_parser = subparsers.add_parser('tune', help='tune using a specific configuration file') tune_parser.add_argument("--device", default=0, type=str) tune_parser.add_argument("--exclude-operations", default = '', type=str) tune_parser.add_argument("--gemm-layouts", default='NN,NT,TN,TT', type=str) tune_parser.add_argument("--gemv-layouts", default='N,T', type=str) tune_parser.add_argument("--viennacl-src-path", default='', type=str) tune_subparsers = tune_parser.add_subparsers(dest='method') simple_parser = tune_subparsers.add_parser('simple', help = 'Tune each operation for unique sizes') simple_parser.add_argument("--blas1-size", default = 10e6, type=int) simple_parser.add_argument("--blas2-size", nargs=2, default=[2560,2560], type=int) simple_parser.add_argument("--blas3-size", nargs=3, default=[1536,1536,1536],type=int) full_parser = tune_subparsers.add_parser('full', help = 'Tune each operation for randomly chosen sizes') full_parser.add_argument("--build-model", default=False, type=bool) full_parser.add_argument("--sample-size", default=30, type=int) args = parser.parse_args() devices = [d for platform in cl.get_platforms() for d in platform.get_devices()] if(args.action=='list-devices'): print("----------------") print("Devices available:") print("----------------") for (i, d) in enumerate(devices): print 'Device', i, '|', cl.device_type.to_string(d.type), '|', d.name, 'on', d.platform.name print("----------------") else: print("------") print("Auto-tuning") print("------") do_tuning(args, devices)