/usr/local/lib64/python3.6/site-packages/caffe2/python
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/fused_8bit_rowwise_conversion_ops_test.py (3945B)
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import numpy as np
import struct
from hypothesis import given
# Eigen/Python round 0.5 away from 0, Numpy rounds to even
round_to_nearest = np.vectorize(round)
def bytes_to_floats(byte_matrix):
floats = np.empty([np.shape(byte_matrix)[0], 1], dtype=np.float32)
for i, byte_values in enumerate(byte_matrix):
floats[i], = struct.unpack('f', bytearray(byte_values))
return floats
def floats_to_bytes(floats):
byte_matrix = np.empty([np.shape(floats)[0], 4], dtype=np.uint8)
for i, value in enumerate(floats):
assert isinstance(value, np.float32), (value, floats)
as_bytes = struct.pack('f', value)
# In Python3 bytes will be a list of int, in Python2 a list of string
if isinstance(as_bytes[0], int):
byte_matrix[i] = list(as_bytes)
else:
byte_matrix[i] = [ord(i) for i in as_bytes]
return byte_matrix
def fused_rowwise_8bit_quantize_reference(data):
minimum = np.min(data, axis=-1, keepdims=True)
maximum = np.max(data, axis=-1, keepdims=True)
span = maximum - minimum
bias = minimum
scale = span / 255.0
inverse_scale = 255.0 / (span + 1e-8)
quantized_data = round_to_nearest((data - bias) * inverse_scale)
scale_bytes = floats_to_bytes(scale.reshape(-1))
scale_bytes = scale_bytes.reshape(data.shape[:-1] + (scale_bytes.shape[-1],))
bias_bytes = floats_to_bytes(bias.reshape(-1))
bias_bytes = bias_bytes.reshape(data.shape[:-1] + (bias_bytes.shape[-1],))
print(quantized_data.shape, scale.shape, scale_bytes.shape, bias.shape, bias_bytes.shape)
return np.concatenate([quantized_data, scale_bytes, bias_bytes], axis=-1)
def fused_rowwise_8bit_quantize_dequantize_reference(data):
fused_quantized = fused_rowwise_8bit_quantize_reference(data)
scale = bytes_to_floats(fused_quantized[..., -8:-4].astype(np.uint8).reshape(-1, 4))
scale = scale.reshape(fused_quantized.shape[:-1] + (scale.shape[-1],))
bias = bytes_to_floats(fused_quantized[..., -4:].astype(np.uint8).reshape(-1, 4))
bias = bias.reshape(fused_quantized.shape[:-1] + (bias.shape[-1],))
quantized_data = fused_quantized[..., :-8]
return quantized_data * scale + bias
class TestFused8BitRowwiseQuantizationConversion(hu.HypothesisTestCase):
@given(input_data=hu.tensor(min_dim=1, max_dim=3, max_value=33))
def test_quantize_op(self, input_data):
quantize = core.CreateOperator(
'FloatToFused8BitRowwiseQuantized',
['input_data'],
['quantized_data'],
)
workspace.FeedBlob('input_data', input_data)
workspace.RunOperatorOnce(quantize)
quantized_data = workspace.FetchBlob('quantized_data')
reference = fused_rowwise_8bit_quantize_reference(
input_data.astype(np.float32)
)
np.testing.assert_array_almost_equal(quantized_data, reference)
@given(input_data=hu.tensor(min_dim=1, max_dim=3, max_value=33))
def test_quantize_and_dequantize_op(self, input_data):
quantize = core.CreateOperator(
'FloatToFused8BitRowwiseQuantized',
['input_data'],
['quantized_data'],
)
workspace.FeedBlob('input_data', input_data)
workspace.RunOperatorOnce(quantize)
quantized_data = workspace.FetchBlob('quantized_data')
dequantize = core.CreateOperator(
'Fused8BitRowwiseQuantizedToFloat',
['quantized_data'],
['dequantized_data'],
)
workspace.FeedBlob('quantized_data', quantized_data)
workspace.RunOperatorOnce(dequantize)
dequantized_data = workspace.FetchBlob('dequantized_data')
reference = fused_rowwise_8bit_quantize_dequantize_reference(input_data)
np.testing.assert_array_almost_equal(dequantized_data, reference)