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# Must happen before importing caffe2.python.*
import caffe2.python.fakelowp.init_shared_libs # noqa
import datetime
import numpy as np
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
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
workspace.GlobalInit(
[
"caffe2",
"--glow_global_fp16=1",
"--glow_global_fused_scale_offset_fp16=1",
"--glow_global_force_sls_fp16_accum=1",
]
)
class Fusions(serial.SerializedTestCase):
@given(
scale=st.floats(1e-4, 1e2),
zp=st.integers(-128, 128),
size=st.integers(1, 100000),
rand_seed=st.integers(0, 65534),
)
@settings(deadline=datetime.timedelta(seconds=10))
def test_tanhquantize(self, scale, zp, size, rand_seed):
np.random.seed(rand_seed)
workspace.ResetWorkspace()
pred_net = caffe2_pb2.NetDef()
pred_net.name = "ref"
pred_net.external_input.append("X")
pred_net.external_output.append("Y_q")
pred_net.op.add().CopyFrom(
core.CreateOperator(
"Tanh", ["X"], ["Y"]
)
)
pred_net.op.add().CopyFrom(
core.CreateOperator(
"Int8Quantize", ["Y"], ["Y_q"], Y_scale=scale, Y_zero_point=zp
)
)
X = np.linspace(-1, 1, size).astype(np.float16).astype(np.float32)
pred_net_onnxified = onnxifi_caffe2_net(
pred_net,
{"X": X.shape},
debug=True,
adjust_batch=False,
use_onnx=False,
)
num_onnxified_ops = sum(
1 if o.type == "Onnxifi" else 0 for o in pred_net_onnxified.op
)
np.testing.assert_equal(num_onnxified_ops, 1)
workspace.FeedBlob("X", X)
workspace.CreateNet(pred_net_onnxified)
workspace.RunNet(pred_net_onnxified.name)
Y_glow = workspace.FetchInt8Blob("Y_q")
ref_net = caffe2_pb2.NetDef()
ref_net.name = "ref"
ref_net.external_input.append("X")
ref_net.external_output.append("Y_q")
ref_net.op.add().CopyFrom(
core.CreateOperator(
"TanhQuantFakeFp16NNPI", ["X"], ["Y_q"], Y_scale=scale, Y_zero_point=zp
)
)
workspace.CreateNet(ref_net)
workspace.RunNet(ref_net.name)
Y_ref = workspace.FetchInt8Blob("Y_q")
if not np.array_equal(Y_ref.data, Y_glow.data) or \
not Y_ref.scale == Y_glow.scale or \
not Y_ref.zero_point == Y_glow.zero_point:
print_test_debug_info(
"tanhfusion",
{
"scale": scale,
"zp": zp,
"input": X,
"ideal nonquant": np.tanh(X),
"Y_glow": Y_glow,
"Y_c2": Y_ref,
}
)
assert(0)
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