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import numpy as np
import caffe2.python.fakelowp.init_shared_libs # noqa
from caffe2.python import core, workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
from caffe2.python.fakelowp.test_utils import print_test_debug_info
import caffe2.python.serialized_test.serialized_test_util as serial
import datetime
from hypothesis import settings
core.GlobalInit(["caffe2", "--caffe2_log_level=-3", "--glow_global_fp16=1"])
class DeqSwishQuantTest(serial.SerializedTestCase):
def _get_scale_zp(self, tensor):
tensor_max = np.max(tensor)
tensor_min = min(0, np.min(tensor))
scale = np.float32(np.float16((tensor_max - tensor_min) / 255.))
zero_point = -tensor_min / scale
zero_point = int(round(np.clip(zero_point, 0, 255.0)))
return (scale, zero_point)
def _sigmoid(self, x):
return 1. / (1. + np.exp(np.float32(-x)))
def _swish(self, x):
return np.float32(x) * self._sigmoid(x)
@settings(deadline=datetime.timedelta(seconds=10))
def test_swish_int8(self):
np.random.seed(0)
workspace.ResetWorkspace()
n = 256
X_fp32 = np.linspace(-20.5, 8., num=n).astype(np.float32).reshape(1, n)
Y_fp32 = self._swish(X_fp32)
X_scale, X_zero_point = self._get_scale_zp(X_fp32)
Y_scale, Y_zero_point = self._get_scale_zp(Y_fp32)
W_fp32 = np.identity(n, dtype=np.float32)
b_fp32 = np.zeros((n,), dtype=np.float32)
workspace.FeedBlob("X", X_fp32)
workspace.FeedBlob("W", W_fp32)
workspace.FeedBlob("b", b_fp32)
workspace.RunOperatorOnce(
core.CreateOperator(
"Int8FCPackWeight",
["W"],
["W_int8"],
engine="DNNLOWP",
save_unpacked_weights=True,
in_scale=X_scale,
)
)
ref_net1 = core.Net("net")
ref_net1.Int8QuantizeNNPI(
["X"],
["X_int8"],
Y_scale=X_scale,
Y_zero_point=X_zero_point
)
ref_net1.Int8FCFakeAcc32NNPI(
["X_int8", "W_int8", "b"],
["U_int8"],
Y_scale=X_scale,
Y_zero_point=X_zero_point,
)
ref_net1.SwishFakeInt8NNPI(
["U_int8"],
["Y"],
X_scale=X_scale,
X_zero_point=X_zero_point,
Y_scale=Y_scale,
Y_zero_point=Y_zero_point
)
ref_net1.Proto().external_output.append("Y")
ref_net = core.Net("net")
ref_net.Int8QuantizeNNPI(
["X"],
["X_int8"],
Y_scale=X_scale,
Y_zero_point=X_zero_point
)
ref_net.Int8FCFakeAcc32NNPI(
["X_int8", "W_int8", "b"],
["U_int8"],
Y_scale=X_scale,
Y_zero_point=X_zero_point,
)
ref_net.Int8DequantizeNNPI(
["U_int8"],
["U_fp16"],
UsingOneOverScale=False
)
ref_net.SwishFakeFp16NNPI(
["U_fp16"],
["Y_fp16"]
)
ref_net.Int8QuantizeNNPI(
["Y_fp16"],
["Y"],
Y_scale=Y_scale,
Y_zero_point=Y_zero_point
)
ref_net.Proto().external_output.append("Y")
# run ref_net
workspace.RunNetOnce(ref_net1)
Y_fbgemm = workspace.FetchInt8Blob("Y")
# run onnxifi net
ref_net.Proto().op[0].type = "Int8Quantize"
ref_net.Proto().op[1].type = "Int8FC"
ref_net.Proto().op[2].type = "Int8Dequantize"
ref_net.Proto().op[3].type = "Swish"
ref_net.Proto().op[4].type = "Int8Quantize"
net_onnxified = onnxifi_caffe2_net(
ref_net.Proto(),
{},
debug=True,
adjust_batch=False,
use_onnx=False,
weight_names=["W_int8", "b"],
)
num_onnxified_ops = sum(
1 if o.type == "Onnxifi" else 0 for o in net_onnxified.op
)
np.testing.assert_equal(num_onnxified_ops, 1)
# TODO: add an assertion to check the optimized net
# fused Dequantize->Swish->Quantize to QuantizedSwish
workspace.CreateNet(net_onnxified)
workspace.RunNet(net_onnxified.name)
Y_glow = workspace.FetchInt8Blob("Y")
U_int8 = workspace.FetchInt8Blob("U_int8")
diff_Y = np.abs(Y_glow.data - Y_fbgemm.data)
num_mismatches = np.count_nonzero(diff_Y)
max_diff = np.max(diff_Y)
if max_diff > 0 or Y_glow.scale != Y_fbgemm.scale or \
Y_glow.zero_point != Y_fbgemm.zero_point:
print_test_debug_info(
"QuantizedSwish",
{
"X": X_fp32,
"X_scale": X_scale,
"X_zero_point": X_zero_point,
"Y_scale": Y_scale,
"Y_zero_point": Y_zero_point,
"U_int8": U_int8,
"Y_fbgemm": Y_fbgemm,
"Y_glow": Y_glow,
"diff": diff_Y,
"max_diff": max_diff,
"num_mismatches": num_mismatches,
},
)
assert 0
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