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from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import numpy as np
def FakeQuantization8BitsRowwise(data):
min_el = np.min(data, axis=1)
max_el = np.max(data, axis=1)
scale = (max_el - min_el) / 255.
bias = min_el
inv_scale = 1. / scale
data = data.T
data = np.round((data - bias) * inv_scale) * scale + bias
return data.T
class TestQuantize8bits(hu.HypothesisTestCase):
def test_quantize_op(self):
op = core.CreateOperator(
'FloatToRowwiseQuantized8Bits',
['input_data'],
['quantized_input', 'scale_bias'])
input_data = np.float32(np.asarray([[801., 786, 235.2, 2353.3434],
[5., 11., 9., -2.]]))
workspace.FeedBlob('input_data', input_data)
workspace.RunOperatorOnce(op)
op1 = core.CreateOperator(
'Rowwise8BitQuantizedToFloat',
['quantized_input', 'scale_bias'],
['dequantized_input'])
workspace.RunOperatorOnce(op1)
result = workspace.FetchBlob('dequantized_input')
ground_truth = FakeQuantization8BitsRowwise(input_data)
np.testing.assert_array_almost_equal(
result, ground_truth)
def test_quantize_tensor_with_const_row_op(self):
op = core.CreateOperator(
'FloatToRowwiseQuantized8Bits',
['input_data'],
['quantized_input', 'scale_bias'])
input_data = np.float32(np.asarray([[801., 786, 235.2, 2353.3434],
[9., 9., 9., 9.]]))
workspace.FeedBlob('input_data', input_data)
workspace.RunOperatorOnce(op)
op1 = core.CreateOperator(
'Rowwise8BitQuantizedToFloat',
['quantized_input', 'scale_bias'],
['dequantized_input'])
workspace.RunOperatorOnce(op1)
result = workspace.FetchBlob('dequantized_input')
ground_truth = FakeQuantization8BitsRowwise(input_data)
ground_truth[1, :] = 9.
np.testing.assert_array_almost_equal(
result, ground_truth)
def test_SparseSegmentUint8(self):
init_net = core.Net("init")
net = core.Net("bench")
size = 10**3
isize = 10**2
# input preparation
d = init_net.UniformFill([], shape=[size, 32])
w = init_net.UniformFill([], shape=[isize, ])
i = init_net.UniformIntFill([], shape=[isize], max=size - 1)
i = init_net.Cast([i], to=core.DataType.INT64)
l = init_net.ConstantFill(
[],
['l'],
shape=[isize // 10],
value=10,
dtype=core.DataType.INT32,
)
net.FloatToRowwiseQuantized8Bits([d],
['quantized_data', 'scale_bias'])
net.Rowwise8BitQuantizedToFloat(['quantized_data', 'scale_bias'],
['dequantized_data'])
# SparseLengthsWeightedSum
net.SparseLengthsWeightedSum(['dequantized_data', w, i, l],
['PositionWeighted_0'], engine='fp16')
net.SparseLengthsWeightedSum8BitsRowwise(
['quantized_data', w, i, l, 'scale_bias'],
['PositionWeighted_1'])
# SparseLengthsSum
net.SparseLengthsSum(['dequantized_data', i, l],
['Sum_0'], engine='fp16')
net.SparseLengthsSum8BitsRowwise(
['quantized_data', i, l, 'scale_bias'],
['Sum_1'])
# SparseLengthsWeightedMean
# net.SparseLengthsWeightedMean(['dequantized_data', w, i, l],
# ['WeightedMean_0'])
# net.SparseLengthsWeightedMean8BitsRowwise(
# ['quantized_data', w, i, l, 'scale_bias'],
# ['WeightedMean_1'])
# SparseLengthsMean
net.SparseLengthsMean(['dequantized_data', i, l],
['Mean_0'], engine='fp16')
net.SparseLengthsMean8BitsRowwise(
['quantized_data', i, l, 'scale_bias'],
['Mean_1'])
gathered_w = net.Gather(['quantized_data', i],
engine='fp16')
gathered_scale_bias = net.Gather(['scale_bias', i],
engine='fp16')
net.Rowwise8BitQuantizedToFloat(
[gathered_w, gathered_scale_bias],
'Gathered_1')
net.Gather(['dequantized_data', i], 'Gathered_0')
workspace.GlobalInit(['caffe2', '--caffe2_log_level=0'])
workspace.RunNetOnce(init_net)
workspace.CreateNet(net)
workspace.RunNetOnce(net)
PositionWeighted_1 = workspace.FetchBlob('PositionWeighted_1')
ground_truth_posw = workspace.FetchBlob('PositionWeighted_0')
np.testing.assert_array_almost_equal(PositionWeighted_1,
ground_truth_posw, decimal=5)
Sum_1 = workspace.FetchBlob('Sum_1')
ground_truth_sum = workspace.FetchBlob('Sum_0')
np.testing.assert_array_almost_equal(Sum_1,
ground_truth_sum, decimal=5)
Mean_1 = workspace.FetchBlob('Mean_1')
ground_truth_mean = workspace.FetchBlob('Mean_0')
np.testing.assert_array_almost_equal(Mean_1,
ground_truth_mean, decimal=5)
Gathered_1 = workspace.FetchBlob('Gathered_1')
ground_truth_gathered = workspace.FetchBlob('Gathered_0')
np.testing.assert_array_almost_equal(Gathered_1,
ground_truth_gathered, decimal=5)
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