#! /usr/bin/python import ck.kernel as ck import copy import re import argparse # Platform tags. platform_tags='mediatek-x20' # Batch size iteration parameters. bs={ 'start':1, 'stop':4, 'step':1, 'default':1 } # OpenBLAS number of threads iteration parameters. nt={ 'start':1, 'stop':4, 'step':1, 'default':1 } # Number of statistical repetitions. num_repetitions=3 def do(i, arg): # Detect basic platform info. ii={'action':'detect', 'module_uoa':'platform', 'out':'out'} r=ck.access(ii) if r['return']>0: return r # Host and target OS params. hos=r['host_os_uoa'] hosd=r['host_os_dict'] tos=r['os_uoa'] tosd=r['os_dict'] tdid=r['device_id'] # Program and command. program='caffe-time' cmd_key='default' # Load Caffe program meta and desc to check deps. ii={'action':'load', 'module_uoa':'program', 'data_uoa':program} rx=ck.access(ii) if rx['return']>0: return rx mm=rx['dict'] # Get compile-time and run-time deps. cdeps=mm.get('compile_deps',{}) rdeps=mm.get('run_deps',{}) # Merge rdeps with cdeps for setting up the pipeline (which uses # common deps), but tag them as "for_run_time". for k in rdeps: cdeps[k]=rdeps[k] cdeps[k]['for_run_time']='yes' # Caffe libs. depl=copy.deepcopy(cdeps['lib-caffe']) if (arg.tos is not None) and (arg.did is not None): tos=arg.tos tdid=arg.did ii={'action':'resolve', 'module_uoa':'env', 'host_os':hos, 'target_os':tos, 'device_id':tdid, 'out':'con', 'deps':{'lib-caffe':copy.deepcopy(depl)} } r=ck.access(ii) if r['return']>0: return r udepl=r['deps']['lib-caffe'].get('choices',[]) # All UOAs of env for Caffe libs. if len(udepl)==0: return {'return':1, 'error':'no installed Caffe libs'} # Caffe models. depm=copy.deepcopy(cdeps['caffemodel']) ii={'action':'resolve', 'module_uoa':'env', 'host_os':hos, 'target_os':tos, 'device_id':tdid, 'out':'con', 'deps':{'caffemodel':copy.deepcopy(depm)} } r=ck.access(ii) if r['return']>0: return r udepm=r['deps']['caffemodel'].get('choices',[]) # All UOAs of env for Caffe models. if len(udepm)==0: return {'return':1, 'error':'no installed Caffe models'} # Prepare pipeline. cdeps['lib-caffe']['uoa']=udepl[0] cdeps['caffemodel']['uoa']=udepm[0] ii={'action':'pipeline', 'prepare':'yes', 'dependencies':cdeps, 'module_uoa':'program', 'data_uoa':program, 'cmd_key':cmd_key, 'env':{ 'CK_CAFFE_SKIP_BACKWARD':1 }, 'target_os':tos, 'device_id':tdid, 'no_state_check':'yes', 'no_compiler_description':'yes', 'skip_calibration':'yes', 'cpu_freq':'max', 'gpu_freq':'max', 'flags':'-O3', 'speed':'no', 'energy':'no', 'skip_print_timers':'yes', 'out':'con' } r=ck.access(ii) if r['return']>0: return r fail=r.get('fail','') if fail=='yes': return {'return':10, 'error':'pipeline failed ('+r.get('fail_reason','')+')'} ready=r.get('ready','') if ready!='yes': return {'return':11, 'error':'pipeline not ready'} state=r['state'] tmp_dir=state['tmp_dir'] # Remember resolved deps for this benchmarking session. xcdeps=r.get('dependencies',{}) # Clean pipeline. if 'ready' in r: del(r['ready']) if 'fail' in r: del(r['fail']) if 'return' in r: del(r['return']) pipeline=copy.deepcopy(r) # For each Caffe lib.******************************************************* for lib_uoa in udepl: # Load Caffe lib. ii={'action':'load', 'module_uoa':'env', 'data_uoa':lib_uoa} r=ck.access(ii) if r['return']>0: return r # Get the tags from e.g. 'BVLC Caffe framework (libdnn,viennacl)' lib_name=r['data_name'] lib_tags=re.match('BVLC Caffe framework \((?P.*)\)', lib_name) lib_tags=lib_tags.group('tags').replace(' ', '').replace(',', '-') # Skip some libs with "in [..]" or "not in [..]". if lib_tags not in [ 'cpu' ]: continue skip_compile='no' # For each Caffe model.************************************************* for model_uoa in udepm: # Load Caffe model. ii={'action':'load', 'module_uoa':'env', 'data_uoa':model_uoa} r=ck.access(ii) if r['return']>0: return r # Get the tags from e.g. 'Caffe model (net and weights) (deepscale, squeezenet, 1.1)' model_name=r['data_name'] model_tags = re.match('Caffe model \(net and weights\) \((?P.*)\)', model_name) model_tags = model_tags.group('tags').replace(' ', '').replace(',', '-') # Skip some models with "in [..]" or "not in [..]". if model_tags not in ['bvlc-alexnet', 'bvlc-googlenet', 'deepscale-squeezenet-1.1']: continue record_repo='local' record_uoa='openblas-threads-'+model_tags+'-'+lib_tags # Prepare pipeline. ck.out('---------------------------------------------------------------------------------------') ck.out('%s - %s' % (lib_name, lib_uoa)) ck.out('%s - %s' % (model_name, model_uoa)) ck.out('Experiment - %s:%s' % (record_repo, record_uoa)) # Prepare autotuning input. cpipeline=copy.deepcopy(pipeline) # Reset deps and change UOA. new_deps={'lib-caffe':copy.deepcopy(depl), 'caffemodel':copy.deepcopy(depm)} new_deps['lib-caffe']['uoa']=lib_uoa new_deps['caffemodel']['uoa']=model_uoa jj={'action':'resolve', 'module_uoa':'env', 'host_os':hos, 'target_os':tos, 'device_id':tdid, 'deps':new_deps} r=ck.access(jj) if r['return']>0: return r cpipeline['dependencies'].update(new_deps) cpipeline['no_clean']=skip_compile cpipeline['no_compile']=skip_compile cpipeline['cmd_key']=cmd_key ii={'action':'autotune', 'module_uoa':'pipeline', 'data_uoa':'program', 'choices_order':[ [ '##choices#env#CK_CAFFE_BATCH_SIZE' ], [ '##choices#env#OPENBLAS_NUM_THREADS' ] ], 'choices_selection':[ {'type':'loop', 'start':bs['start'], 'stop':bs['stop'], 'step':bs['step'], 'default':bs['default']}, {'type':'loop', 'start':nt['start'], 'stop':nt['stop'], 'step':nt['step'], 'default':nt['default']} ], 'features_keys_to_process':[ '##choices#env#CK_CAFFE_BATCH_SIZE', '##choices#env#OPENBLAS_NUM_THREADS' ], 'iterations':-1, 'repetitions':num_repetitions, 'record':'yes', 'record_failed':'yes', 'record_params':{ 'search_point_by_features':'yes' }, 'record_repo':record_repo, 'record_uoa':record_uoa, 'tags':[ 'explore-batch-size-openblas-threads', program, model_tags, lib_tags, platform_tags ], 'pipeline':cpipeline, 'out':'con'} r=ck.access(ii) if r['return']>0: return r fail=r.get('fail','') if fail=='yes': return {'return':10, 'error':'pipeline failed ('+r.get('fail_reason','')+')'} skip_compile='yes' return {'return':0} parser = argparse.ArgumentParser(description='Pipeline') parser.add_argument("--target_os", action="store", dest="tos") parser.add_argument("--device_id", action="store", dest="did") myarg=parser.parse_args() r=do({}, myarg) if r['return']>0: ck.err(r)