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): device = args['device'] dname = misc_tools.sanitize_string(device.name) if os.path.isfile(args['json-file']): json_out = json.load(open(args['json-file'], 'r')) else: 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-sizes'], 'reduction': args['blas1-sizes'], 'matrix-axpy' : args['blas2-sizes'], 'row-wise-reduction' : args['blas2-sizes'], 'matrix-product': args['blas3-sizes']} 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'] 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) 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)])) #Helper for tuning def tune(execution_handler, a, b, dimsample, 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_generator = log_space_gen_product(a, b, args['sample-size'], dimsample) 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) 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('float64').tolist(), '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]) D['profiles'] = [map(int, x) for x in profiles] #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, 1e4, 1e7, 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, 1e4, 1e7, 1, ()) #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), (), sizes, fname, parameters) tune(execution_handler, 100, 4000, 2, ()) #Row-wise reduction if operation=='row-wise-reduction': for A_trans in args['gemv-layouts']: 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, 100, 4000, 2, (A_trans,)) #Matrix Product if operation=='matrix-product': for L in args['gemm-layouts']: A_trans = L[0] B_trans = L[1] def execution_handler(sizes, fname=os.devnull, parameters=None): A = vcl.Matrix((sizes[0], sizes[2]) if A_trans=='N' else (sizes[2],sizes[0]), context=ctx, dtype=datatype, layout=vcl.COL_MAJOR) B = vcl.Matrix((sizes[2], sizes[1]) if B_trans=='N' else (sizes[1],sizes[2]), 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[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)) json.dump(json_out, open(args['json-file'],'w')) if __name__ == "__main__": devices = [d for platform in cl.get_platforms() for d in platform.get_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("----------------") args = {} def add_input(help, default): return raw_input(help + "[" + default + "] : ") or default args['device'] = devices[int(add_input('Device to tune for','0'))] args['exclude-operations'] = add_input('Operations to exclude','vector-axpy,matrix-axpy,reduction,row-wise-reduction,matrix-product-float64').split(',') if not 'matrix-product' in args['exclude-operations']: args['gemm-layouts'] = add_input('GEMM Layouts', 'NN,NT,TN,TT').split(',') if not 'row-wise-reduction' in args['exclude-operations']: args['gemv-layouts'] = add_input('GEMV Layouts', 'N,T').split(',') args['json-file'] = add_input('JSON File', misc_tools.sanitize_string(args['device'].name) + '.json') args['method'] = add_input('Tuning type', 'simple') if args['method'] == 'simple': args['blas1-sizes'] = [int(float(add_input('BLAS1 size', '10e6')))] args['blas2-sizes'] = map(int, add_input('BLAS2 sizes (M,N)', '2560,2560').split(',')) args['blas3-sizes'] = map(int, add_input('BLAS3 sizes (M,N,K)', '1024,1024,1024').split(',')) args['build-model'] = True args['sample-size'] = 30 args['viennacl-src-path'] = '' print("------") print("Auto-tuning") print("------") do_tuning(args)