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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, utils, workspace
from caffe2.quantization.server import utils as dnnlowp_utils
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 DNNLowPOpGroupNormTest(hu.HypothesisTestCase):
@given(
N=st.integers(0, 4),
G=st.integers(2, 4),
K=st.integers(2, 12),
H=st.integers(4, 16),
W=st.integers(4, 16),
order=st.sampled_from(["NCHW", "NHWC"]),
in_quantized=st.booleans(),
out_quantized=st.booleans(),
weight_quantized=st.booleans(),
**hu.gcs_cpu_only
)
def test_dnnlowp_group_norm(
self,
N,
G,
K,
H,
W,
order,
in_quantized,
out_quantized,
weight_quantized,
gc,
dc,
):
C = G * K
X = np.random.rand(N, C, H, W).astype(np.float32) * 5.0 - 1.0
if order == "NHWC":
X = utils.NCHW2NHWC(X)
gamma = np.random.rand(C).astype(np.float32) * 2.0 - 1.0
beta = np.random.randn(C).astype(np.float32) - 0.5
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("GroupNorm", ""),
("GroupNorm", "DNNLOWP"),
("Int8GroupNorm", "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
do_quantize_weight = (
engine == "DNNLOWP" and weight_quantized and len(outputs) > 0
)
if do_quantize:
quantize = core.CreateOperator(
"Quantize", ["X"], ["X_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize])
if do_quantize_weight:
int8_given_tensor_fill, gamma_q_param = dnnlowp_utils.create_int8_given_tensor_fill(
gamma, "gamma_q"
)
net.Proto().op.extend([int8_given_tensor_fill])
X_min = 0 if X.size == 0 else X.min()
X_max = 0 if X.size == 0 else X.max()
X_q_param = dnnlowp_utils.choose_quantization_params(X_min, X_max)
int8_bias_tensor_fill = dnnlowp_utils.create_int8_bias_tensor_fill(
beta, "beta_q", X_q_param, gamma_q_param
)
net.Proto().op.extend([int8_bias_tensor_fill])
group_norm = core.CreateOperator(
op_type,
[
"X_q" if do_quantize else "X",
"gamma_q" if do_quantize_weight else "gamma",
"beta_q" if do_quantize_weight else "beta",
],
["Y_q" if do_dequantize else "Y"],
dequantize_output=0 if do_dequantize else 1,
group=G,
order=order,
is_test=True,
engine=engine,
device_option=gc,
)
if do_quantize_weight:
# When quantized weight is provided, we can't rescale the
# output dynamically by looking at the range of output of each
# batch, so here we provide the range of output observed from
# fp32 reference implementation
dnnlowp_utils.add_quantization_param_args(group_norm, outputs[0][0])
net.Proto().op.extend([group_norm])
if do_dequantize:
dequantize = core.CreateOperator(
"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.create_blob("gamma").feed(gamma, device_option=gc)
self.ws.create_blob("beta").feed(beta, 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, atol_scale=2.0)
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