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from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import math
MAX_TEST_EMBEDDING_SIZE = 20
MAX_TEST_SEQUENCE_LENGTH = 10
MAX_TEST_BATCH_SIZE = 5
MIN_TEST_ALPHA = 5000.0
MAX_TEST_ALPHA = 20000.0
MIN_TEST_AMPLITUDE = 0.1
MAX_TEST_AMPLITUDE = 10.0
class TestSinusoidPositionEncodingOp(serial.SerializedTestCase):
@given(
positions_vec=hu.arrays(
dims=[MAX_TEST_SEQUENCE_LENGTH],
dtype=np.int32,
elements=st.integers(1, MAX_TEST_SEQUENCE_LENGTH)
),
embedding_size=st.integers(1, MAX_TEST_EMBEDDING_SIZE),
batch_size=st.integers(1, MAX_TEST_BATCH_SIZE),
alpha=st.floats(MIN_TEST_ALPHA, MAX_TEST_ALPHA),
amplitude=st.floats(MIN_TEST_AMPLITUDE, MAX_TEST_AMPLITUDE),
**hu.gcs_cpu_only
)
@settings(deadline=10000)
def test_sinusoid_embedding(
self, positions_vec, embedding_size, batch_size, alpha, amplitude, gc, dc
):
positions = np.tile(positions_vec, [batch_size, 1]).transpose()
op = core.CreateOperator(
"SinusoidPositionEncoding",
["positions"],
["output"],
embedding_size=embedding_size,
alpha=alpha,
amplitude=amplitude,
)
def sinusoid_encoding(dim, position):
x = 1. * position / math.pow(alpha, 1. * dim / embedding_size)
if dim % 2 == 0:
return amplitude * math.sin(x)
else:
return amplitude * math.cos(x)
def sinusoid_embedding_op(positions):
output_shape = (len(positions), len(positions[0]), embedding_size)
ar = np.zeros(output_shape)
for i, position_vector in enumerate(positions):
for j, position in enumerate(position_vector):
for k in range(embedding_size):
ar[i, j, k] = sinusoid_encoding(k, position)
return [ar]
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[positions],
reference=sinusoid_embedding_op,
)
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