def test_dnnlowp_quantize(self, size, is_empty, absorb, gc, dc):
if is_empty:
size = 0
min_ = -10.0
max_ = 20.0
X = (np.random.rand(size) * (max_ - min_) + min_).astype(np.float32)
X_min = 0 if X.size == 0 else X.min()
X_max = 1 if X.size == 0 else X.max()
X_scale = (max(X_max, 0) - min(X_min, 0)) / 255
X_zero = np.round(-X_min / X_scale)
op_type_list = ["Quantize", "Int8Quantize"]
engine = "DNNLOWP"
for op_type in op_type_list:
net = core.Net("test_net")
quantize = core.CreateOperator(
op_type, ["X"], ["X_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize])
dnnlowp_pybind11.CreateInt8QuantParamsBlob(
"quant_param", float(X_scale), int(X_zero)
)
quantize_2 = core.CreateOperator(
op_type,
["X", "quant_param"],
["X_q_2"],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize_2])
if absorb:
net_str = dnnlowp_pybind11.freeze_quantization_params(
net.Proto().SerializeToString())
net.Proto().ParseFromString(net_str)
workspace.FeedBlob("X", X, device_option=gc)
workspace.RunNetOnce(net)
X_q = workspace.FetchInt8Blob("X_q")[0]
X_q_2 = workspace.FetchInt8Blob("X_q_2")[0]
# Dequantize results and measure quantization error against inputs
X_dq = X_scale * (X_q - X_zero)
X_dq_2 = X_scale * (X_q_2 - X_zero)
# should be divided by 2 in an exact math, but divide by 1.9 here
# considering finite precision in floating-point numbers
atol = X_scale / 1.9
np.testing.assert_allclose(X_dq, X, atol=atol, rtol=0)
np.testing.assert_allclose(X_dq_2, X, atol=atol, rtol=0)
def run_conv_or_fc(
test_case,
init_net,
net,
X,
W,
b,
op_type,
engine,
order,
gc,
outputs,
scale=None,
zero_point=None,
):
if order:
# Conv
Output = collections.namedtuple("Output",
["Y", "op_type", "engine", "order"])
else:
# FC
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
# We run DNNLOWP ops multiple times to test their first runs that
# do caching so exercises different code paths from the subsequent
# runs
# self.ws.run re-creates operator every time so this test covers
# cases when we have multiple nets sharing the same workspace
test_case.ws.create_blob("X").feed(X, device_option=gc)
test_case.ws.create_blob("W").feed(W, device_option=gc)
test_case.ws.create_blob("b").feed(b, device_option=gc)
if scale is not None and zero_point is not None:
with workspace.WorkspaceGuard(test_case.ws):
dnnlowp_pybind11.CreateInt8QuantParamsBlob("quant_param",
float(scale),
int(zero_point))
if init_net:
test_case.ws.run(init_net)
for i in range(1 if engine == "" else 2):
test_case.ws.run(net)
Y = test_case.ws.blobs["Y"].fetch()
if order:
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
else:
outputs.append(Output(Y=Y, op_type=op_type, engine=engine))
# workspace.CreateNet + workspace.RunNet reuses the same operator
if engine != "":
workspace.FeedBlob("X", X)
workspace.FeedBlob("W", W)
workspace.FeedBlob("b", b)
if scale is not None and zero_point is not None:
dnnlowp_pybind11.CreateInt8QuantParamsBlob("quant_param",
float(scale),
int(zero_point))
if init_net:
workspace.RunNetOnce(init_net)
workspace.CreateNet(net)
for i in range(2):
workspace.RunNet(net)
Y = workspace.FetchBlob("Y")
if order:
outputs.append(
Output(Y=Y, op_type=op_type, engine=engine, order=order))
else:
outputs.append(Output(Y=Y, op_type=op_type, engine=engine))