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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
def compare_rowwise(emb_orig, emb_reconstructed, fp16):
# there is an absolute error introduced per row through int8 quantization
# and a relative error introduced when quantizing back from fp32 to fp16
assert emb_orig.shape == emb_reconstructed.shape
rtol = 1e-8
if fp16:
rtol = 1e-3
erange = np.amax(emb_orig, axis=1) - np.amin(emb_orig, axis=1)
threshold = erange / 255.0 / 1.9
for i in range(emb_orig.shape[0]):
r_orig = emb_orig[i, :]
r_reconstructed = emb_reconstructed[i, :]
isclose = np.isclose(r_orig, r_reconstructed, atol=threshold[i], rtol=rtol)
n_violated = isclose.size - isclose.sum()
if n_violated > 0:
print(isclose, threshold[i])
print(i, r_orig, r_reconstructed, threshold[i], r_orig - r_reconstructed)
assert n_violated == 0
class TestLengthsReducerOpsFused8BitRowwise(hu.HypothesisTestCase):
@given(
num_rows=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(),
fp16=st.booleans(),
)
def test_sparse_lengths_sum(
self, num_rows, blocksize, weighted, seed, empty_indices, fp16
):
net = core.Net("bench")
np.random.seed(seed)
if fp16:
input_data = np.random.rand(num_rows, blocksize).astype(np.float16)
else:
input_data = np.random.rand(num_rows, blocksize).astype(np.float32)
if empty_indices:
lengths = np.zeros(num_rows, dtype=np.int32)
num_indices = 0
else:
num_indices = np.random.randint(len(input_data))
# the number of indices per sample
lengths_split = np.clip(num_indices // 2, 1, 10)
lengths = (
np.ones([num_indices // lengths_split], dtype=np.int32) * lengths_split
)
# readjust num_indices when lengths_split doesn't divide num_indices
num_indices = num_indices // lengths_split * lengths_split
indices = np.random.randint(
low=0, high=len(input_data), size=[num_indices], dtype=np.int32
)
weights = np.random.uniform(size=[len(indices)]).astype(np.float32)
if fp16:
quantized_data = net.HalfFloatToFused8BitRowwiseQuantized(
"input_data", "quantized_data"
)
dequantized_data = net.Fused8BitRowwiseQuantizedToHalfFloat(
quantized_data, "dequantized_data"
)
else:
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)
dequantized_data = workspace.FetchBlob("dequantized_data")
np.testing.assert_array_almost_equal(
input_data, workspace.FetchBlob("input_data")
)
compare_rowwise(input_data, dequantized_data, fp16)
sum_reference = workspace.FetchBlob("sum_reference")
sum_quantized = workspace.FetchBlob("sum_quantized")
if fp16:
np.testing.assert_array_almost_equal(
sum_reference, sum_quantized, decimal=3
)
else:
np.testing.assert_array_almost_equal(sum_reference, sum_quantized)
@given(
num_rows=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(),
fp16=st.booleans(),
)
def test_sparse_lengths_mean(self, num_rows, blocksize, seed, empty_indices, fp16):
net = core.Net("bench")
np.random.seed(seed)
if fp16:
input_data = np.random.rand(num_rows, blocksize).astype(np.float16)
else:
input_data = np.random.rand(num_rows, blocksize).astype(np.float32)
if empty_indices:
lengths = np.zeros(num_rows, dtype=np.int32)
num_indices = 0
else:
num_indices = np.random.randint(len(input_data))
# the number of indices per sample
lengths_split = np.clip(num_indices // 2, 1, 10)
lengths = (
np.ones([num_indices // lengths_split], dtype=np.int32) * lengths_split
)
# readjust num_indices when lengths_split doesn't divide num_indices
num_indices = num_indices // lengths_split * lengths_split
indices = np.random.randint(
low=0, high=len(input_data), size=[num_indices], dtype=np.int32
)
print(indices, lengths)
if fp16:
quantized_data = net.HalfFloatToFused8BitRowwiseQuantized(
"input_data", "quantized_data"
)
dequantized_data = net.Fused8BitRowwiseQuantizedToHalfFloat(
quantized_data, "dequantized_data"
)
else:
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)
dequantized_data = workspace.FetchBlob("dequantized_data")
np.testing.assert_array_almost_equal(
input_data, workspace.FetchBlob("input_data")
)
compare_rowwise(input_data, dequantized_data, fp16)
mean_reference = workspace.FetchBlob("mean_reference")
mean_quantized = workspace.FetchBlob("mean_quantized")
if fp16:
np.testing.assert_array_almost_equal(
mean_reference, mean_quantized, decimal=3
)
else:
np.testing.assert_array_almost_equal(mean_reference, mean_quantized)
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