def test_sym_nnvm(batch_size, iter_num):
logger = logging.getLogger("log.test.nnvm")
logger.info("=== Log Test NNVM ===")
sym_file, param_file, ext_file = load_fname("_darknet53_voc", "mrt.all.quantize", True)
sym, params = mx.sym.load(sym_file), nd.load(param_file)
inputs_ext, _ = sim.load_ext(ext_file)
nnvm_sym, nnvm_params = spass.mxnet_to_nnvm(sym, params, inputs_ext)
spass.cvm_build(nnvm_sym, nnvm_params, inputs_ext, *load_fname("_darknet53_voc", "nnvm"))
def std_dump(sym,
params,
inputs_ext,
data,
model_name,
is_mxnet=True,
batch=False,
data_dtype="int8",
max_num=20,
dump_ops=[]):
if not batch:
for k, v in inputs_ext.items():
v['shape'] = (1, *v['shape'][1:])
data = data[0].reshape(inputs_ext['data']['shape'])
datadir = "/data/std_out/" + model_name
os.makedirs(datadir, exist_ok=True)
if is_mxnet:
data = sim.load_real_data(data, 'data', inputs_ext)
inputs_ext['data']['data'] = data
spass.sym_dump_layer_outputs(sym,
params,
inputs_ext,
datadir,
data_dtype=data_dtype,
max_num=max_num,
dump_ops=dump_ops,
ctx=mx.gpu(0))
sym, params = spass.mxnet_to_nnvm(sym, params, inputs_ext)
else:
tvm_graph, tvm_params, lib = spass.cvm_build(sym,
params,
inputs_ext,
"/dev/null",
"/dev/null",
runtime="tvm",
target="llvm",
dtype="int32")
model = graph_runtime.create(tvm_graph, lib, tvm.cpu())
model.set_input(**params)
model.set_input("data", data)
model.run()
np.save(datadir + "/data.npy", data.asnumpy().astype('int8'))
for i in range(len(sym.list_output_names())):
out = model.get_output(i).asnumpy()
np.save("%s/result_%d.npy" % (datadir, i), out)
return spass.cvm_build(sym, params, inputs_ext, datadir + "/symbol",
datadir + "/params")
def test_sym_pass(batch_size=10, iter_num=10, quantize=True):
logger = logging.getLogger("log.test.sym.pass")
calib_ctx = mx.gpu(1)
ctx = [mx.gpu(int(i)) for i in "1,2,3,4".split(',') if i.strip()]
inputs_ext = {
'data': {
'shape': (batch_size, 3, 224, 224),
}
}
inputs = [mx.sym.var(name) for name in inputs_ext]
logger.info("load dataset, symbol and parameters")
# load dataset and iter function
data_iter = ds.load_imagenet_rec(batch_size)
def data_iter_func():
data = data_iter.next()
return data.data[0], data.label[0]
data, _ = data_iter_func()
# load original model for accuracy
net1 = utils.load_model(*load_fname(version), inputs, ctx=ctx)
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
acc_top1.reset()
acc_top5.reset()
def shufflenet(data, label):
data = gluon.utils.split_and_load(data,
ctx_list=ctx,
batch_axis=0,
even_split=False)
res = [net1.forward(d) for d in data]
res = nd.concatenate(res)
acc_top1.update(label, res)
_, top1 = acc_top1.get()
acc_top5.update(label, res)
_, top5 = acc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
if quantize:
# load original model
sym_fname, param_fname = load_fname(version)
sym, params = mx.sym.load(sym_fname), nd.load(param_fname)
sym, params = spass.sym_quant_prepare(sym, params, inputs_ext)
# quantize process
mrt = _mrt.MRT(sym, params, inputs_ext) # initialize
mrt.set_data('data', data) # set input data
mrt.calibrate(ctx=calib_ctx) # calibration
mrt.set_output_prec(8) # set output prec, do nothing by default
qsym, qparams, inputs_ext = mrt.quantize() # quantization
# dump quantized model
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize",
True)
sim.save_ext(dump_ext, inputs_ext)
nd.save(dump_params, qparams)
open(dump_sym, "w").write(qsym.tojson())
if False:
# convert to cvm executor model
inputs_ext['data']['shape'] = (1, 3, 224, 224)
nnvm_sym, nnvm_params = spass.mxnet_to_nnvm(qsym, qparams, inputs_ext)
spass.cvm_build(nnvm_sym, nnvm_params, inputs_ext,
*load_fname(version, "nnvm"))
# load quantized model for accuracy
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
(inputs_ext, ) = sim.load_ext(dump_ext)
inputs = [mx.sym.var(n) for n in inputs_ext]
net3 = utils.load_model(dump_sym, dump_params, inputs, ctx=ctx)
# net3 = mx.gluon.nn.SymbolBlock(qsym, inputs)
# utils.load_parameters(net3, qparams, ctx=ctx)
qacc_top1 = mx.metric.Accuracy()
qacc_top5 = mx.metric.TopKAccuracy(5)
qacc_top1.reset()
qacc_top5.reset()
def cvm_quantize(data, label):
data = sim.load_real_data(data, 'data', inputs_ext)
data = gluon.utils.split_and_load(data,
ctx_list=ctx,
batch_axis=0,
even_split=False)
res = [net3.forward(d) for d in data]
res = nd.concatenate(res)
qacc_top1.update(label, res)
_, top1 = qacc_top1.get()
qacc_top5.update(label, res)
_, top5 = qacc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
# compare accuracy between models
utils.multi_validate(shufflenet,
data_iter_func,
cvm_quantize,
iter_num=iter_num,
logger=logger)
def test_mx_quantize(batch_size=10, iter_num=10):
logger = logging.getLogger("log.test.mx.quantize")
ctx = [mx.gpu(int(i)) for i in "1,3".split(',') if i.strip()]
inputs_ext = { 'data': {
'shape': (batch_size, 3, 224, 224),
}}
inputs = [mx.sym.var(n) for n in inputs_ext]
data_iter = ds.load_imagenet_rec(batch_size)
def data_iter_func():
data = data_iter.next()
return data.data[0], data.label[0]
data, _ = data_iter_func()
net1 = utils.load_model(*load_fname(version), inputs, ctx=ctx)
acc_top1 = mx.metric.Accuracy()
acc_top5 = mx.metric.TopKAccuracy(5)
acc_top1.reset()
acc_top5.reset()
def mobilenet(data, label):
data = gluon.utils.split_and_load(data, ctx_list=ctx, batch_axis=0, even_split=False)
res = [net1.forward(d) for d in data]
res = nd.concatenate(res)
acc_top1.update(label, res)
_, top1 = acc_top1.get()
acc_top5.update(label, res)
_, top5 = acc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
calib_ctx = mx.gpu(1)
sym_fname, param_fname = load_fname(version)
sym, params = mx.sym.load(sym_fname), nd.load(param_fname)
sym, params = spass.sym_quant_prepare(sym, params, inputs_ext)
if True:
if True:
mrt = _mrt.MRT(sym, params, inputs_ext)
mrt.set_data('data', data)
mrt.calibrate()
# [ 0.0008745864 0.03330660510427334 ] 0.6670066884888368 0.7753906
# mrt.set_threshold("mobilenet0_dense0_weight", 0.67)
# # [ -0.0036011334 0.054821780899052534 ] 1.100036751338784 1.4626989
# mrt.set_threshold("mobilenet0_conv24_batchnorm24_fwd_weight", 1.1)
# # [ 0.013243316 1.7543557133786065 ] 70.18747185088569 94.66275
# mrt.set_threshold("mobilenet0_conv23_batchnorm23_fwd_weight", 35.10)
# # [ -0.0016149869 0.05713169649243355 ] 1.1442489167675376 1.7122083
# mrt.set_threshold("mobilenet0_conv20_batchnorm20_fwd_weight", 1.144)
# # [ -0.0015804865 0.04523811489343643 ] 0.9063427844084799 1.0745146
# mrt.set_threshold("mobilenet0_conv16_batchnorm16_fwd_weight", 0.90)
# # [ 0.4315614 2.447332109723772 ] 49.37820360490254 63.959927
# mrt.set_threshold("mobilenet0_conv2_batchnorm2_fwd", 49.37)
# # [ 0.9770754 1.3392452512468611 ] 27.761980422905516 40.729546
# mrt.set_threshold("mobilenet0_relu2_fwd", 27.76)
# [ 1.0975745 1.0489919010632773 ] 22.077412493692915 23.784576
# mrt.set_threshold("mobilenet0_relu4_fwd", 22.08)
# # [ 0.9885562 2.360489403014386 ] 48.19834426651407 69.22121
# mrt.set_threshold("mobilenet0_conv5_batchnorm5_fwd", 48.2)
# # [ 0.7895588 1.0544661745870065 ] 21.878882319617176 30.95745
# mrt.set_threshold("mobilenet0_relu17_fwd", 21.88)
# # [ 0.8717863 1.0887600296120434 ] 22.646986888608513 28.265652
# mrt.set_threshold("mobilenet0_relu19_fwd", 22.65)
# # [ 0.35124516 0.6501711574631898 ] 13.354668314135012 20.770807
# mrt.set_threshold("mobilenet0_relu20_fwd", 13.35)
# # [ 0.9378179 1.110470714216975 ] 23.147232155910086 27.886068
# mrt.set_threshold("mobilenet0_relu21_fwd", 23.15)
# # [ 0.36263302 0.6352599878026505 ] 13.067832775738754 17.18809
# mrt.set_threshold("mobilenet0_relu22_fwd", 13.07)
# # [ 0.19875833 0.49999100821358816 ] 10.198578498193196 16.625143
# mrt.set_threshold("mobilenet0_relu24_fwd", 10.2)
# # [ 0.32357717 1.6308352606637138 ] 65.55698759215218 75.84912
# mrt.set_threshold("mobilenet0_conv25_batchnorm25_fwd", 32.94)
# # [ 0.36793178 1.512995992388044 ] 30.62785163096019 49.464615
# mrt.set_threshold("mobilenet0_relu26_fwd", 30.63)
# # [ 18.028658 38.61970520019531 ] 790.4227619171143 805.51886
# mrt.set_threshold("sum0", 790.423)
mrt.set_output_prec(8)
qsym, qparams, inputs_ext = mrt.quantize()
else:
inputs_ext['data']['data'] = data
th_dict = calib.sym_calibrate(sym, params, inputs_ext, ctx=calib_ctx)
qsym, qparams, precs, _ = calib.sym_simulate(sym, params, inputs_ext, th_dict)
qsym, qparams = calib.sym_realize(qsym, qparams, inputs_ext, precs)
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
sim.save_ext(dump_ext, inputs_ext)
nd.save(dump_params, qparams)
open(dump_sym, "w").write(qsym.tojson())
dump_sym, dump_params = load_fname(version, "nnvm.compile")
nnvm_sym, nnvm_params = spass.mxnet_to_nnvm(qsym, qparams, inputs_ext)
spass.cvm_build(nnvm_sym, nnvm_params, inputs_ext, dump_sym, dump_params)
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
(inputs_ext,) = sim.load_ext(dump_ext)
net2 = utils.load_model(dump_sym, dump_params, inputs, ctx=ctx)
qacc_top1 = mx.metric.Accuracy()
qacc_top5 = mx.metric.TopKAccuracy(5)
qacc_top1.reset()
qacc_top5.reset()
def cvm_quantize(data, label):
data = sim.load_real_data(data, 'data', inputs_ext)
data = gluon.utils.split_and_load(data, ctx_list=ctx, batch_axis=0, even_split=False)
res = [net2.forward(d) for d in data]
res = nd.concatenate(res)
qacc_top1.update(label, res)
_, top1 = qacc_top1.get()
qacc_top5.update(label, res)
_, top5 = qacc_top5.get()
return "top1={:6.2%} top5={:6.2%}".format(top1, top5)
utils.multi_validate(mobilenet, data_iter_func,
cvm_quantize,
iter_num=iter_num, logger=logger)
mrt.set_data('data', data) # set input data
mrt.calibrate(ctx=calib_ctx) # calibration
mrt.set_output_prec(8) # set output prec, do nothing by default
qsym, qparams, inputs_ext = mrt.quantize() # quantization
if False:
# dump quantized model
dump_sym, dump_params, dump_ext = load_fname(version, "sym.quantize", True)
sim.save_ext(dump_ext, inputs_ext)
nd.save(dump_params, qparams)
open(dump_sym, "w").write(qsym.tojson())
# convert to cvm executor model
inputs_ext['data']['shape'] = (1, 3, input_size, input_size)
nnvm_sym, nnvm_params = spass.mxnet_to_nnvm(qsym, qparams, inputs_ext)
spass.cvm_build(nnvm_sym, nnvm_params, inputs_ext,
*load_fname(version, "nnvm"))
# load quantized model for accuracy
net3 = mx.gluon.nn.SymbolBlock(qsym, inputs)
utils.load_parameters(net3, qparams, ctx=ctx)
qacc_top1 = mx.metric.Accuracy()
qacc_top5 = mx.metric.TopKAccuracy(5)
qacc_top1.reset()
qacc_top5.reset()
def cvm_quantize(data, label):
data = sim.load_real_data(data, 'data', inputs_ext)
data = gluon.utils.split_and_load(data,
ctx_list=ctx,
batch_axis=0,
elif op_name in attr_scales:
scales = attr_scales[op_name]
else:
return node
for k, v in scales.items():
assert k in attr, "attribute %s not in %s(%s) with %s" \
% (k, op_name, name, attr.keys())
attr[k] = int(float(attr[k]) * oscales_dict[v])
node = sutils.get_mxnet_op(op_name)(*childs, **attr, name=name)
return node
maps = mrt.get_maps()
qsym, qparams = _mrt.merge_model(qsym, qparams, sym2, prm2, maps, op_scales)
cvm_flag = cfg["cvm"]
cvm_batch_size = cvm_flag["batch_size"]
cvm_batch_size = batch_size if cvm_batch_size == -1 else cvm_batch_size
shp = tuple(cvm_batch_size if s == -1 else s for s in input_shape)
inputs_ext["data"]["shape"] = shp
nnvm_sym, nnvm_params = spass.mxnet_to_nnvm(qsym, qparams, inputs_ext)
cvm_dir = cfg["cvm"]["dir"]
spass.cvm_build(nnvm_sym, nnvm_params, inputs_ext,
path.join(cvm_dir, "cvm.symbol"),
path.join(cvm_dir, "cvm.params"))
if cfg["cvm"]["save_ext"]:
sim.save_ext(path.join(cvm_dir, "cvm.ext"), inputs_ext, oscales)