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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 DNNLowPAddOpTest(hu.HypothesisTestCase):
@given(
N=st.integers(32, 256),
is_empty=st.booleans(),
in_quantized=st.booleans(),
out_quantized=st.booleans(),
in_place=st.sampled_from([(False, False), (True, False), (False, True)]),
**hu.gcs_cpu_only
)
def test_dnnlowp_elementwise_add_int(
self, N, is_empty, in_quantized, out_quantized, in_place, gc, dc
):
if is_empty:
N = 0
# FIXME: DNNLOWP Add doesn't support inplace operation and
# dequantize_output=1 at the same time
if in_place[0] or in_place[1]:
in_quantized = True
out_quantized = True
# A has scale 1, so exactly represented after quantization
min_ = -100
max_ = min_ + 255
A = np.round(np.random.rand(N) * (max_ - min_) + min_)
A = A.astype(np.float32)
if N != 0:
A[0] = min_
A[1] = max_
# B has scale 1/2, so exactly represented after quantization
B = np.round(np.random.rand(N) * 255 / 2 - 64).astype(np.float32)
if N != 0:
B[0] = -64
B[1] = 127.0 / 2
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [("Add", ""), ("Add", "DNNLOWP"), ("Int8Add", "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_A = core.CreateOperator(
"Quantize", ["A"], ["A_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize_A])
quantize_B = core.CreateOperator(
"Quantize", ["B"], ["B_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize_B])
out = "Y"
if in_place[0]:
out = "A"
elif in_place[1]:
out = "B"
add = core.CreateOperator(
op_type,
["A_q", "B_q"] if do_quantize else ["A", "B"],
[(out + "_q") if do_dequantize else out],
dequantize_output=not do_dequantize,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([add])
if do_dequantize:
dequantize = core.CreateOperator(
"Dequantize", [out + "_q"], [out], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
self.ws.create_blob("A").feed(A, device_option=gc)
self.ws.create_blob("B").feed(B, device_option=gc)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs[out].fetch(), op_type=op_type, engine=engine)
)
check_quantized_results_close(outputs)
@given(**hu.gcs_cpu_only)
def test_dnnlowp_elementwise_add_broadcast(self, gc, dc):
# Set broadcast and no axis, i.e. broadcasting last dimensions.
min_ = -100
max_ = min_ + 255
A = np.round(np.random.rand(2, 3, 4, 5) * (max_ - min_) + min_)
A = A.astype(np.float32)
A[0, 0, 0, 0] = min_
A[0, 0, 0, 1] = max_
B = np.round(np.random.rand(4, 5) * 255 / 2 - 64).astype(np.float32)
B[0, 0] = -64
B[0, 1] = 127.0 / 2
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [("Add", ""), ("Add", "DNNLOWP"), ("Int8Add", "DNNLOWP")]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
add = core.CreateOperator(
op_type,
["A", "B"],
["Y"],
engine=engine,
device_option=gc,
broadcast=1,
dequantize_output=1,
)
net.Proto().op.extend([add])
self.ws.create_blob("A").feed(A, device_option=gc)
self.ws.create_blob("B").feed(B, device_option=gc)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(), op_type=op_type, engine=engine)
)
check_quantized_results_close(outputs)
@given(**hu.gcs_cpu_only)
def test_dnnlowp_elementwise_add_broadcast_axis(self, gc, dc):
for bdim, axis in [
((3, 4), 1), # broadcasting intermediate dimensions
((2,), 0), # broadcasting the first dimension
((1, 4, 1), 1),
]:
# broadcasting with single elem dimensions at both ends
min_ = -100
max_ = min_ + 255
A = np.round(np.random.rand(2, 3, 4, 5) * (max_ - min_) + min_)
A = A.astype(np.float32)
B = np.round(np.random.rand(*bdim) * 255 / 2 - 64).astype(np.float32)
A.flat[0] = min_
A.flat[1] = max_
B.flat[0] = -64
B.flat[1] = 127.0 / 2
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [("Add", ""), ("Add", "DNNLOWP"), ("Int8Add", "DNNLOWP")]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
add = core.CreateOperator(
op_type,
["A", "B"],
["Y"],
engine=engine,
device_option=gc,
broadcast=1,
axis=axis,
dequantize_output=1,
)
net.Proto().op.extend([add])
self.ws.create_blob("A").feed(A, device_option=gc)
self.ws.create_blob("B").feed(B, device_option=gc)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(), op_type=op_type, engine=engine)
)
check_quantized_results_close(outputs)
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