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lengths_reducer_fused_8bit_rowwise_ops_test.py
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from __future__ import absolute_import, division, print_function, unicode_literals
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, workspace
from hypothesis import given
class TestLengthsReducerOpsFused8BitRowwise(hu.HypothesisTestCase):
@given(
batchsize=st.integers(1, 20),
blocksize=st.sampled_from([8, 16, 32, 64, 85, 96, 128, 163]),
weighted=st.booleans(),
seed=st.integers(0, 2 ** 32 - 1),
empty_indices=st.booleans(),
)
def test_sparse_lengths_sum(
self, batchsize, blocksize, weighted, seed, empty_indices
):
net = core.Net("bench")
np.random.seed(seed)
input_data = np.random.rand(batchsize, blocksize).astype(np.float32)
if empty_indices:
indices = np.empty(0, dtype=np.int32)
else:
indices = np.random.randint(
low=0,
high=len(input_data),
size=[np.random.randint(len(input_data))],
dtype=np.int32,
)
weights = np.random.uniform(size=[len(indices)]).astype(np.float32)
lengths_split = np.clip(1, len(indices) // 2, 10)
lengths = (
np.ones([len(indices) // lengths_split], dtype=np.int32) * lengths_split
)
quantized_data = net.FloatToFused8BitRowwiseQuantized(
"input_data", "quantized_data"
)
dequantized_data = net.Fused8BitRowwiseQuantizedToFloat(
quantized_data, "dequantized_data"
)
if weighted:
net.SparseLengthsWeightedSum(
[dequantized_data, "weights", "indices", "lengths"],
"sum_reference",
)
net.SparseLengthsWeightedSumFused8BitRowwise(
[quantized_data, "weights", "indices", "lengths"], "sum_quantized"
)
else:
net.SparseLengthsSum(
[dequantized_data, "indices", "lengths"], "sum_reference",
)
net.SparseLengthsSumFused8BitRowwise(
[quantized_data, "indices", "lengths"], "sum_quantized"
)
workspace.FeedBlob("input_data", input_data)
workspace.FeedBlob("weights", weights)
workspace.FeedBlob("indices", indices)
workspace.FeedBlob("lengths", lengths)
workspace.GlobalInit(["caffe2", "--caffe2_log_level=0"])
workspace.CreateNet(net)
workspace.RunNetOnce(net)
sum_reference = workspace.FetchBlob("sum_reference")
sum_quantized = workspace.FetchBlob("sum_quantized")
np.testing.assert_array_almost_equal(sum_reference, sum_quantized)
@given(
batchsize=st.integers(1, 20),
blocksize=st.sampled_from([8, 16, 32, 64, 85, 96, 128, 163]),
seed=st.integers(0, 2 ** 32 - 1),
empty_indices=st.booleans(),
)
def test_sparse_lengths_mean(self, batchsize, blocksize, seed, empty_indices):
net = core.Net("bench")
np.random.seed(seed)
input_data = np.random.rand(batchsize, blocksize).astype(np.float32)
if empty_indices:
indices = np.empty(0, dtype=np.int32)
lengths = np.zeros(batchsize, dtype=np.int32)
else:
indices = np.random.randint(
low=0,
high=len(input_data),
size=[np.random.randint(len(input_data))],
dtype=np.int32,
)
lengths_split = np.clip(1, len(indices) // 2, 10)
lengths = (
np.ones([len(indices) // lengths_split], dtype=np.int32) * lengths_split
)
print(indices, lengths)
quantized_data = net.FloatToFused8BitRowwiseQuantized(
"input_data", "quantized_data"
)
dequantized_data = net.Fused8BitRowwiseQuantizedToFloat(
quantized_data, "dequantized_data"
)
net.SparseLengthsMean(
[dequantized_data, "indices", "lengths"], "mean_reference"
)
net.SparseLengthsMeanFused8BitRowwise(
[quantized_data, "indices", "lengths"], "mean_quantized"
)
workspace.FeedBlob("input_data", input_data)
workspace.FeedBlob("indices", indices)
workspace.FeedBlob("lengths", lengths)
workspace.GlobalInit(["caffe2", "--caffe2_log_level=0"])
workspace.CreateNet(net)
workspace.RunNetOnce(net)
mean_reference = workspace.FetchBlob("mean_reference")
mean_quantized = workspace.FetchBlob("mean_quantized")
np.testing.assert_array_almost_equal(mean_reference, mean_quantized)