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import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
class DNNLowPGatherOpTest(hu.HypothesisTestCase):
@given(
dim1=st.integers(256, 512),
dim2=st.integers(32, 256),
is_empty=st.booleans(),
in_quantized=st.booleans(),
out_quantized=st.booleans(),
**hu.gcs_cpu_only
)
def test_dnnlowp_gather(self, dim1, dim2, is_empty, in_quantized, out_quantized, gc, dc):
if is_empty:
dim2 = 0
# FIXME : DNNLOWP Gather doesn't support quantized input and
# dequantized output
if in_quantized:
out_quantized = True
data = (np.random.rand(dim1) * 2 - 1).astype(np.float32)
index = np.floor(np.random.rand(dim2) * dim1).astype(np.int32)
Output = collections.namedtuple("Output", ["out", "op_type", "engine"])
outputs = []
op_engine_list = [
("Gather", ""),
("Gather", "DNNLOWP"),
("Int8Gather", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine and in_quantized
do_dequantize = "DNNLOWP" in engine and out_quantized
if do_quantize:
quantize_data = core.CreateOperator(
"Quantize", ["data"], ["data_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize_data])
gather = core.CreateOperator(
op_type,
["data_q" if do_quantize else "data", "index"],
["out_q" if do_dequantize else "out"],
dequantize_output=not do_dequantize,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([gather])
if do_dequantize:
dequantize = core.CreateOperator(
"Dequantize", ["out_q"], ["out"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
self.ws.create_blob("data").feed(data, device_option=gc)
self.ws.create_blob("index").feed(index, device_option=gc)
self.ws.run(net)
outputs.append(
Output(out=self.ws.blobs["out"].fetch(), op_type=op_type, engine=engine)
)
check_quantized_results_close(outputs, ref=data)
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