143 lines
5.6 KiB
Python
143 lines
5.6 KiB
Python
import random, argparse, json, os
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from math import log, isinf
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from itertools import chain, product
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from numpy import argsort, argmax
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from operator import mul
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import isaac as sc
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from external.sklearn.forest import RandomForestRegressor
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import optimize, tools, model
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from json import encoder
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import json
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encoder.FLOAT_REPR = lambda o: format(o, '.2f')
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encoder.separators = (',',':')
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def unique(L):
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seen = set()
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seen_add = seen.add
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return [ x for x in L if not (x in seen or seen_add(x))]
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def pow2range(a, b):
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return [2**x for x in range(a, b)]
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def tune(device, operation, json_path):
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#Context
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context = sc.driver.context(device)
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#List of size tuples to use
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sizes = {}
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sizes[sc.templates.axpy] = [(x,) for x in tools.expspace(1e3, 1e8, 4)]
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sizes[sc.templates.gemv_n] = product(pow2range(4,17), pow2range(4,17))
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sizes[sc.templates.gemv_t] = sizes[sc.templates.gemv_n]
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sizes[sc.templates.gemm_nn] = product(pow2range(6, 12), pow2range(6, 12), pow2range(6, 12))
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sizes[sc.templates.gemm_tn] = sizes[sc.templates.gemm_nn]
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sizes[sc.templates.gemm_nt] = sizes[sc.templates.gemm_nn]
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sizes[sc.templates.gemm_tt] = sizes[sc.templates.gemm_nn]
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#Quick tuning - AlexNet sizes + Intuition
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sizes[sc.templates.ger] = [(1536,1536)]
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sizes[sc.templates.gemv_n] = [(1000,256),
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(4096,256)]
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sizes[sc.templates.gemv_t] = [(169,256),
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(169,384),
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(729,256),
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(3025,96)]
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sizes[sc.templates.gemm_nn] = [(3025,96,363),
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(729,128,1200),
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(169,384,2304),
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(169,192,1728),
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(169,128,1728)]
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sizes[sc.templates.gemm_nt] = [(169,1728,128),
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(169,1728,192),
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(169,2304,384),
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(729,1200,128)]
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sizes[sc.templates.gemm_tn] = [(1728,128,169),
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(1728,192,169),
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(2304,384,169),
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(1200,128,729),
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(363,96,3025)]
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#Remove duplicated
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sizes = unique(list(sizes[operation]))
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sizes = [x for x in sizes if 1e-4 <= tools.memory_footprint(operation, x) <= 1e-1]
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#Training data
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performance = tools.metric_of(operation)
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profiles = []
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X = []
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Y = []
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for idx, x in enumerate(sizes):
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print x
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nparams = len(profiles)
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tree, operands = tools.tree_of(operation, x, context)
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#Check if the current best prediction is not a local optimum
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if idx==0:
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tune = True
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predicted = None
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else:
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if nparams==1:
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predicted = profiles[0]
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else:
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clf = RandomForestRegressor(min(10, idx+1), max_depth=min(10, idx+1)).fit(X, Y)
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#clf, nrmse = model.train(X, Y, profiles)
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predperf = clf.predict(x)[0]
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best = (-predperf).argsort()[:5]
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perf = [performance(x, tools.benchmark(operation, profiles[b], tree)) for b in best]
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predicted = profiles[best[argmax(perf)]]
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#tune = not optimize.is_local_optimum(predicted, operation, x, context)
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tune = True
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#Retune if necessary
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if tune:
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#new = optimize.exhaustive(operation, x, context)
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new = optimize.genetic(operation, x, context, niter=1000, naccept=1000, popsize=20, prior=predicted)[0]
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if new not in profiles:
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profiles.append(new)
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if idx > 0:
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for xx,yy in zip(X, Y):
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_tree, _operands = tools.tree_of(operation, xx, context)
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try:
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time = tools.benchmark(operation, new, _tree)
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perf = performance(xx, time)
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except (sc.OperationNotSupported, sc.LaunchOutOfResources, sc.MemObjectAllocationFailure):
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perf = 0
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yy.append(0 if isinf(perf) else perf)
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#Update dataset
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y = []
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fastest = max(predperf) if nparams > 1 else None
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for ip, p in enumerate(profiles):
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try:
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perf = 0 if fastest and ip < nparams and predperf[ip]/fastest < .1 else performance(x,tools.benchmark(operation, p, tree))
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except (sc.OperationNotSupported, sc.LaunchOutOfResources, sc.MemObjectAllocationFailure):
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perf = 0
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y.append(0 if isinf(perf) else perf)
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X.append(x)
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Y.append(y)
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#Export to JSON
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json_path = tools.sanitize(device.name) + '.json' if not json_path else json_path
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if os.path.isfile(json_path):
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json_data = json.load(open(json_path, 'r'))
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else:
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json_data = {}
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json_data["version"] = "1.0"
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operation_name = operation.__name__
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if operation_name not in json_data:
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json_data[operation_name] = {}
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json_data[operation_name]['float32'] = {}
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D = json_data[operation_name]['float32']
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if len(profiles) > 1:
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clf, nrmse = model.train(X, Y, profiles)
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D['predictor'] = [{'children_left': e.tree_.children_left.tolist(),
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'children_right': e.tree_.children_right.tolist(),
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'threshold': e.tree_.threshold.astype('float64').tolist(),
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'feature': e.tree_.feature.astype('float64').tolist(),
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'value': e.tree_.value[:,:,0].astype('float64').tolist()} for e in clf.estimators_]
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D['profiles'] = [map(int, x) for x in profiles]
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json.dump(json_data, open(json_path,'w'))
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