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# Copyright 2022 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Integration tests for the full RASP -> transformer compilation."""
from absl.testing import absltest
from absl.testing import parameterized
import jax
import numpy as np
from tracr.compiler import compiling
from tracr.compiler import lib
from tracr.compiler import test_cases
from tracr.craft import tests_common
from tracr.rasp import rasp
_COMPILER_BOS = "rasp_to_transformer_integration_test_BOS"
_COMPILER_PAD = "rasp_to_transformer_integration_test_PAD"
# Force float32 precision on TPU, which otherwise defaults to float16.
jax.config.update("jax_default_matmul_precision", "float32")
class CompilerIntegrationTest(tests_common.VectorFnTestCase):
def assertSequenceEqualWhenExpectedIsNotNone(self, actual_seq, expected_seq):
for actual, expected in zip(actual_seq, expected_seq):
if expected is not None and actual != expected:
self.fail(f"{actual_seq} does not match (ignoring Nones) "
f"expected_seq={expected_seq}")
@parameterized.named_parameters(
dict(
testcase_name="map",
program=rasp.Map(lambda x: x + 1, rasp.tokens)),
dict(
testcase_name="sequence_map",
program=rasp.SequenceMap(lambda x, y: x + y, rasp.tokens,
rasp.indices)),
dict(
testcase_name="sequence_map_with_same_input",
program=rasp.SequenceMap(lambda x, y: x + y, rasp.tokens,
rasp.indices)),
dict(
testcase_name="select_aggregate",
program=rasp.Aggregate(
rasp.Select(rasp.tokens, rasp.tokens, rasp.Comparison.EQ),
rasp.Map(lambda x: 1, rasp.tokens))))
def test_rasp_program_and_transformer_produce_same_output(self, program):
vocab = {0, 1, 2}
max_seq_len = 3
assembled_model = compiling.compile_rasp_to_model(
program, vocab, max_seq_len, compiler_bos=_COMPILER_BOS)
test_outputs = {}
rasp_outputs = {}
for val in vocab:
test_outputs[val] = assembled_model.apply([_COMPILER_BOS, val]).decoded[1]
rasp_outputs[val] = program([val])[0]
with self.subTest(val=0):
self.assertEqual(test_outputs[0], rasp_outputs[0])
with self.subTest(val=1):
self.assertEqual(test_outputs[1], rasp_outputs[1])
with self.subTest(val=2):
self.assertEqual(test_outputs[2], rasp_outputs[2])
@parameterized.named_parameters(*test_cases.TEST_CASES)
def test_compiled_models_produce_expected_output(self, program, vocab,
test_input, expected_output,
max_seq_len, **kwargs):
del kwargs
assembled_model = compiling.compile_rasp_to_model(
program, vocab, max_seq_len, compiler_bos=_COMPILER_BOS)
test_output = assembled_model.apply([_COMPILER_BOS] + test_input)
if isinstance(expected_output[0], (int, float)):
np.testing.assert_allclose(
test_output.decoded[1:], expected_output, atol=1e-7, rtol=0.005)
else:
self.assertSequenceEqualWhenExpectedIsNotNone(test_output.decoded[1:],
expected_output)
@parameterized.named_parameters(*test_cases.CAUSAL_TEST_CASES)
def test_compiled_causal_models_produce_expected_output(
self, program, vocab, test_input, expected_output, max_seq_len, **kwargs):
del kwargs
assembled_model = compiling.compile_rasp_to_model(
program,
vocab,
max_seq_len,
causal=True,
compiler_bos=_COMPILER_BOS,
compiler_pad=_COMPILER_PAD)
test_output = assembled_model.apply([_COMPILER_BOS] + test_input)
if isinstance(expected_output[0], (int, float)):
np.testing.assert_allclose(
test_output.decoded[1:], expected_output, atol=1e-7, rtol=0.005)
else:
self.assertSequenceEqualWhenExpectedIsNotNone(test_output.decoded[1:],
expected_output)
@parameterized.named_parameters(
dict(
testcase_name="reverse_1",
program=lib.make_reverse(rasp.tokens),
vocab={"a", "b", "c", "d"},
test_input=list("abcd"),
expected_output=list("dcba"),
max_seq_len=5),
dict(
testcase_name="reverse_2",
program=lib.make_reverse(rasp.tokens),
vocab={"a", "b", "c", "d"},
test_input=list("abc"),
expected_output=list("cba"),
max_seq_len=5),
dict(
testcase_name="reverse_3",
program=lib.make_reverse(rasp.tokens),
vocab={"a", "b", "c", "d"},
test_input=list("ad"),
expected_output=list("da"),
max_seq_len=5),
dict(
testcase_name="reverse_4",
program=lib.make_reverse(rasp.tokens),
vocab={"a", "b", "c", "d"},
test_input=["c"],
expected_output=["c"],
max_seq_len=5),
dict(
testcase_name="length_categorical_1",
program=rasp.categorical(lib.make_length()),
vocab={"a", "b", "c", "d"},
test_input=list("abc"),
expected_output=[3, 3, 3],
max_seq_len=5),
dict(
testcase_name="length_categorical_2",
program=rasp.categorical(lib.make_length()),
vocab={"a", "b", "c", "d"},
test_input=list("ad"),
expected_output=[2, 2],
max_seq_len=5),
dict(
testcase_name="length_categorical_3",
program=rasp.categorical(lib.make_length()),
vocab={"a", "b", "c", "d"},
test_input=["c"],
expected_output=[1],
max_seq_len=5),
dict(
testcase_name="length_numerical_1",
program=rasp.numerical(lib.make_length()),
vocab={"a", "b", "c", "d"},
test_input=list("abc"),
expected_output=[3, 3, 3],
max_seq_len=5),
dict(
testcase_name="length_numerical_2",
program=rasp.numerical(lib.make_length()),
vocab={"a", "b", "c", "d"},
test_input=list("ad"),
expected_output=[2, 2],
max_seq_len=5),
dict(
testcase_name="length_numerical_3",
program=rasp.numerical(lib.make_length()),
vocab={"a", "b", "c", "d"},
test_input=["c"],
expected_output=[1],
max_seq_len=5),
)
def test_compiled_models_produce_expected_output_with_padding(
self, program, vocab, test_input, expected_output, max_seq_len, **kwargs):
del kwargs
assembled_model = compiling.compile_rasp_to_model(
program,
vocab,
max_seq_len,
compiler_bos=_COMPILER_BOS,
compiler_pad=_COMPILER_PAD)
pad_len = (max_seq_len - len(test_input))
test_input = test_input + [_COMPILER_PAD] * pad_len
test_input = [_COMPILER_BOS] + test_input
test_output = assembled_model.apply(test_input)
output = test_output.decoded
output_len = len(output)
output_stripped = test_output.decoded[1:output_len - pad_len]
self.assertEqual(output[0], _COMPILER_BOS)
if isinstance(expected_output[0], (int, float)):
np.testing.assert_allclose(
output_stripped, expected_output, atol=1e-7, rtol=0.005)
else:
self.assertEqual(output_stripped, expected_output)
if __name__ == "__main__":
absltest.main()
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