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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 DNNLowPOpSumOpTest(hu.HypothesisTestCase):
# correctness test with no quantization error in inputs
@given(
N=st.integers(32, 256),
M=st.integers(1, 3),
is_empty=st.booleans(),
**hu.gcs_cpu_only
)
def test_dnnlowp_elementwise_sum_int(self, N, M, is_empty, gc, dc):
if is_empty:
N = 0
# All inputs have scale 1, so exactly represented after quantization
inputs = M * [None]
X_names = M * [None]
X_q_names = M * [None]
for i in range(M):
X = np.random.randint(-128, 127, N, np.int8).astype(np.float32)
if N != 0:
X[0] = -128
X[-1] = 127
inputs[i] = X
X_names[i] = chr(ord("A") + i)
X_q_names[i] = X_names[i] + "_q"
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [("Sum", ""), ("Sum", "DNNLOWP"), ("Int8Sum", "DNNLOWP")]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if engine == "DNNLOWP":
for i in range(M):
quantize = core.CreateOperator(
"Quantize",
X_names[i],
X_q_names[i],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
sum_ = core.CreateOperator(
op_type,
X_q_names if engine == "DNNLOWP" else X_names,
["Y_q" if engine == "DNNLOWP" else "Y"],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([sum_])
if engine == "DNNLOWP":
dequantize = core.CreateOperator(
"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
for i in range(M):
self.ws.create_blob(X_names[i]).feed(X, 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)
# correctness test with no quantization error in inputs
@given(N=st.integers(32, 256), M=st.integers(1, 3), **hu.gcs_cpu_only)
def test_dnnlowp_elementwise_sum_int_inplace(self, N, M, gc, dc):
# All inputs have scale 1, so exactly represented after quantization
inputs = M * [None]
X_names = M * [None]
X_q_names = M * [None]
for i in range(M):
X = np.random.randint(-128, 127, N, np.int8).astype(np.float32)
X[0] = -128
X[-1] = 127
inputs[i] = X
X_names[i] = chr(ord("A") + i)
X_q_names[i] = X_names[i] + "_q"
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [("Sum", ""), ("Sum", "DNNLOWP"), ("Int8Sum", "DNNLOWP")]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if engine == "DNNLOWP":
for i in range(M):
quantize = core.CreateOperator(
"Quantize",
X_names[i],
X_q_names[i],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
sum_ = core.CreateOperator(
op_type,
X_q_names if engine == "DNNLOWP" else X_names,
[X_q_names[0] if engine == "DNNLOWP" else X_names[0]],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([sum_])
if engine == "DNNLOWP":
dequantize = core.CreateOperator(
"Dequantize",
[X_q_names[0]],
[X_names[0]],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([dequantize])
for i in range(M):
self.ws.create_blob(X_names[i]).feed(X, device_option=gc)
self.ws.run(net)
outputs.append(
Output(
Y=self.ws.blobs[X_names[0]].fetch(), op_type=op_type, engine=engine
)
)
check_quantized_results_close(outputs)
# correctness test with no quantization error in inputs
@given(N=st.integers(32, 256), M=st.integers(1, 3), **hu.gcs_cpu_only)
def test_dnnlowp_elementwise_sum_relu_int(self, N, M, gc, dc):
# All inputs have scale 1, so exactly represented after quantization
inputs = M * [None]
X_names = M * [None]
X_q_names = M * [None]
for i in range(M):
X = np.random.randint(-128, 127, N, np.int8).astype(np.float32)
X[0] = -128
X[-1] = 127
inputs[i] = X
X_names[i] = chr(ord("A") + i)
X_q_names[i] = X_names[i] + "_q"
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("SumRelu", ""),
("SumRelu", "DNNLOWP"),
("Int8SumRelu", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if engine == "DNNLOWP":
for i in range(M):
quantize = core.CreateOperator(
"Quantize",
X_names[i],
X_q_names[i],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
sum_relu = core.CreateOperator(
op_type,
X_q_names if engine == "DNNLOWP" else X_names,
["Y_q" if engine == "DNNLOWP" else "Y"],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([sum_relu])
if engine == "DNNLOWP":
dequantize = core.CreateOperator(
"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
for i in range(M):
self.ws.create_blob(X_names[i]).feed(X, 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)
# correctness test with no quantization error in inputs
@given(N=st.integers(32, 256), M=st.integers(1, 3), **hu.gcs_cpu_only)
def test_dnnlowp_elementwise_sum_relu_int_inplace(self, N, M, gc, dc):
# All inputs have scale 1, so exactly represented after quantization
inputs = M * [None]
X_names = M * [None]
X_q_names = M * [None]
for i in range(M):
X = np.random.randint(-128, 127, N, np.int8).astype(np.float32)
X[0] = -128
X[-1] = 127
inputs[i] = X
X_names[i] = chr(ord("A") + i)
X_q_names[i] = X_names[i] + "_q"
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("SumRelu", ""),
("SumRelu", "DNNLOWP"),
("Int8SumRelu", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if engine == "DNNLOWP":
for i in range(M):
quantize = core.CreateOperator(
"Quantize",
X_names[i],
X_q_names[i],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
sum_relu = core.CreateOperator(
op_type,
X_q_names if engine == "DNNLOWP" else X_names,
[X_q_names[0] if engine == "DNNLOWP" else X_names[0]],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([sum_relu])
if engine == "DNNLOWP":
dequantize = core.CreateOperator(
"Dequantize",
[X_q_names[0]],
[X_names[0]],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([dequantize])
for i in range(M):
self.ws.create_blob(X_names[i]).feed(X, device_option=gc)
self.ws.run(net)
outputs.append(
Output(
Y=self.ws.blobs[X_names[0]].fetch(), op_type=op_type, engine=engine
)
)
check_quantized_results_close(outputs)
|
