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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 import utils as dnnlowp_utils
from caffe2.quantization.server.dnnlowp_test_utils import (
avoid_vpmaddubsw_overflow_fc,
check_quantized_results_close,
run_conv_or_fc,
)
from hypothesis import given
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
class DNNLowPFullyConnectedOpTest(hu.HypothesisTestCase):
# correctness test with no quantization error in inputs
@given(
input_channels=st.sampled_from([3, 4, 5, 8, 16, 32]),
output_channels=st.integers(2, 16),
batch_size=st.integers(0, 16),
in_quantized=st.booleans(),
out_quantized=st.booleans(),
weight_quantized=st.booleans(),
prepack_weight=st.booleans(),
preserve_activation_sparsity=st.booleans(),
preserve_weight_sparsity=st.booleans(),
fuse_relu=st.booleans(),
output_packed_bias=st.booleans(),
use_input_qparam=st.booleans(),
use_output_qparam=st.booleans(),
**hu.gcs_cpu_only
)
def test_dnnlowp_fully_connected_int(
self,
input_channels,
output_channels,
batch_size,
in_quantized,
out_quantized,
weight_quantized,
prepack_weight,
preserve_activation_sparsity,
preserve_weight_sparsity,
fuse_relu,
output_packed_bias,
use_input_qparam,
use_output_qparam,
gc,
dc,
):
# X and W have scale 1, so exactly represented after quantization
X_min = 0 if preserve_activation_sparsity else -77
X_max = X_min + 255
X = np.round(
np.random.rand(batch_size, input_channels) * (X_max - X_min) + X_min
)
X = X.astype(np.float32)
# input channels 0 and 1 are all X_min to avoid overflow from vpmaddubsw
# when multiplied with W_min and W_max
X[:, 0] = X_min
if batch_size != 0:
X[0, 1] = X_max
if preserve_weight_sparsity:
W_min = -128
W_max = 100
else:
W_min = -100
W_max = W_min + 255
W = np.round(
np.random.rand(output_channels, input_channels) * (W_max - W_min) + W_min
)
W = W.astype(np.float32)
W[0, 0] = W_min
W[1, 0] = W_max
# Make sure we won't have overflows from vpmaddubsw instruction used in
# fbgemm
avoid_vpmaddubsw_overflow_fc(
batch_size,
input_channels,
output_channels,
X,
X_min,
X_max,
W,
W_min,
W_max,
)
b = np.random.randn(output_channels).astype(np.float32)
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [("FC", "", False, False)]
if fuse_relu:
op_engine_list += [("Int8FCRelu", "DNNLOWP", False, False)]
else:
op_engine_list += [
# type, engine, do_fuse, skip_requantization
("FC", "DNNLOWP", False, False),
("FC", "DNNLOWP_16", False, False),
("Int8FC", "DNNLOWP", False, False),
("Int8FC", "DNNLOWP", True, False),
("Int8FC", "DNNLOWP", False, True),
("Int8FC", "DNNLOWP", True, True),
]
for op_type, engine, do_fuse, skip_requantization in op_engine_list:
init_net = core.Net("test_init_net")
net = core.Net("test_net")
do_quantize = "DNNLOWP" in engine and in_quantized and not do_fuse
do_dequantize = "DNNLOWP" in engine and out_quantized and not skip_requantization
do_quantize_weight = (
engine == "DNNLOWP" and weight_quantized and len(outputs) > 0
)
do_prepack_weight = engine == "DNNLOWP" and prepack_weight
if do_quantize:
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
preserve_activation_sparsity=preserve_activation_sparsity,
engine=engine,
device_option=gc,
)
net.Proto().op.extend([quantize])
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, preserve_activation_sparsity
)
w_q_param = None
if do_quantize_weight:
(
int8_given_tensor_fill,
w_q_param,
) = dnnlowp_utils.create_int8_given_tensor_fill(
W, "W_q", preserve_weight_sparsity
)
init_net.Proto().op.extend([int8_given_tensor_fill])
# Bias
int8_bias_tensor_fill = dnnlowp_utils.create_int8_bias_tensor_fill(
b, "b_q", x_q_param, w_q_param
)
init_net.Proto().op.extend([int8_bias_tensor_fill])
if do_prepack_weight:
inputs = ["W_q" if do_quantize_weight else "W"]
if do_dequantize:
inputs += ["b_q" if do_quantize_weight else "b"]
pack = core.CreateOperator(
"Int8FCPackWeight",
inputs,
["W_packed", "B_q32"]
if do_dequantize and output_packed_bias
else ["W_packed"],
preserve_weight_sparsity=preserve_weight_sparsity,
in_scale=x_q_param.scale,
engine=engine,
)
init_net.Proto().op.extend([pack])
fc = core.CreateOperator(
op_type,
[
"X_q" if do_quantize else "X",
"W_packed"
if do_prepack_weight
else ("W_q" if do_quantize_weight else "W"),
"b_q" if do_quantize_weight else "b",
# "quant_param",
],
["Y_q" if do_dequantize else "Y"],
dequantize_output=not do_dequantize,
preserve_activation_sparsity=preserve_activation_sparsity,
preserve_weight_sparsity=preserve_weight_sparsity,
engine=engine,
device_option=gc,
)
if op_type != "FC":
if (do_dequantize and use_output_qparam) or (use_input_qparam and op_type == "Int8_FC"):
fc.input.extend(["quant_param"])
if (use_input_qparam and op_type == "Int8_FC"):
fc.input.extend(["X_quant_param"])
if do_quantize_weight or do_prepack_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(
fc, outputs[0][0], preserve_activation_sparsity
)
net.Proto().op.extend([fc])
if fuse_relu and "DNNLOWP" not in engine:
net.Relu(["Y"], "Y")
if do_dequantize:
dequantize = core.CreateOperator(
"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
if use_output_qparam and do_dequantize and op_type != "FC":
ref_output = outputs[0][0]
ref_output_min = 0 if ref_output.size == 0 else ref_output.min()
ref_output_max = 0 if ref_output.size == 0 else ref_output.max()
q_param = dnnlowp_utils.choose_quantization_params(
ref_output_min, ref_output_max, preserve_activation_sparsity
)
q_param_scale = q_param.scale
q_param_zero_point = q_param.zero_point
else:
q_param_scale = None
q_param_zero_point = None
if not (use_input_qparam and op_type == "Int8FC"):
x_q_param_scale = None
x_q_param_zero_point = None
else:
x_q_param_scale = x_q_param.scale
x_q_param_zero_point = x_q_param.zero_point
run_conv_or_fc(
self,
init_net,
net,
X,
W,
b,
op_type,
engine,
None,
gc,
outputs,
q_param_scale,
q_param_zero_point,
x_q_param_scale,
x_q_param_zero_point,
)
if output_packed_bias and do_prepack_weight and do_dequantize:
bias_int32 = self.ws.blobs["B_q32"].fetch()
if do_quantize_weight:
np.testing.assert_equal(
bias_int32[0], np.round(b / (x_q_param.scale * w_q_param.scale))
)
np.testing.assert_equal(bias_int32[0].dtype, np.int32)
shapes, types = workspace.InferShapesAndTypes(
[init_net, net],
blob_dimensions={
"X": [batch_size, input_channels],
"W": [output_channels, input_channels],
"b": [output_channels],
"quant_param": [1],
"X_quant_param": [1],
},
blob_types={
"X": core.DataType.FLOAT,
"W": core.DataType.FLOAT,
"b": core.DataType.FLOAT,
"quant_param": core.DataType.FLOAT,
"X_quant_param": core.DataType.FLOAT,
},
)
assert (
"Y" in shapes and "Y" in types
), "Failed to infer the shape or type of Y"
self.assertEqual(shapes["Y"], [batch_size, output_channels])
self.assertEqual(types["Y"], core.DataType.FLOAT)
check_quantized_results_close(outputs, symmetric=preserve_activation_sparsity)
|
