Spaces:
Sleeping
Sleeping
File size: 9,346 Bytes
9bdaa77 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 |
# 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 RASP -> craft stages of the compiler."""
import unittest
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
from tracr.compiler import basis_inference
from tracr.compiler import craft_graph_to_model
from tracr.compiler import expr_to_craft_graph
from tracr.compiler import nodes
from tracr.compiler import rasp_to_graph
from tracr.compiler import test_cases
from tracr.craft import bases
from tracr.craft import tests_common
from tracr.rasp import rasp
_BOS_DIRECTION = "rasp_to_transformer_integration_test_BOS"
_ONE_DIRECTION = "rasp_to_craft_integration_test_ONE"
def _make_input_space(vocab, max_seq_len):
tokens_space = bases.VectorSpaceWithBasis.from_values("tokens", vocab)
indices_space = bases.VectorSpaceWithBasis.from_values(
"indices", range(max_seq_len))
one_space = bases.VectorSpaceWithBasis.from_names([_ONE_DIRECTION])
bos_space = bases.VectorSpaceWithBasis.from_names([_BOS_DIRECTION])
input_space = bases.join_vector_spaces(tokens_space, indices_space, one_space,
bos_space)
return input_space
def _embed_input(input_seq, input_space):
bos_vec = input_space.vector_from_basis_direction(
bases.BasisDirection(_BOS_DIRECTION))
one_vec = input_space.vector_from_basis_direction(
bases.BasisDirection(_ONE_DIRECTION))
embedded_input = [bos_vec + one_vec]
for i, val in enumerate(input_seq):
i_vec = input_space.vector_from_basis_direction(
bases.BasisDirection("indices", i))
val_vec = input_space.vector_from_basis_direction(
bases.BasisDirection("tokens", val))
embedded_input.append(i_vec + val_vec + one_vec)
return bases.VectorInBasis.stack(embedded_input)
def _embed_output(output_seq, output_space, categorical_output):
embedded_output = []
output_label = output_space.basis[0].name
for x in output_seq:
if x is None:
out_vec = output_space.null_vector()
elif categorical_output:
out_vec = output_space.vector_from_basis_direction(
bases.BasisDirection(output_label, x))
else:
out_vec = x * output_space.vector_from_basis_direction(
output_space.basis[0])
embedded_output.append(out_vec)
return bases.VectorInBasis.stack(embedded_output)
class CompilerIntegrationTest(tests_common.VectorFnTestCase):
@parameterized.named_parameters(
dict(
testcase_name="map",
program=rasp.categorical(rasp.Map(lambda x: x + 1, rasp.tokens))),
dict(
testcase_name="sequence_map",
program=rasp.categorical(
rasp.SequenceMap(lambda x, y: x + y, rasp.tokens, rasp.indices))),
dict(
testcase_name="sequence_map_with_same_input",
program=rasp.categorical(
rasp.SequenceMap(lambda x, y: x + y, rasp.tokens, rasp.tokens))),
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_craft_model_produce_same_output(self, program):
vocab = {0, 1, 2}
max_seq_len = 3
extracted = rasp_to_graph.extract_rasp_graph(program)
basis_inference.infer_bases(
extracted.graph,
extracted.sink,
vocab,
max_seq_len=max_seq_len,
)
expr_to_craft_graph.add_craft_components_to_rasp_graph(
extracted.graph,
bos_dir=bases.BasisDirection(_BOS_DIRECTION),
one_dir=bases.BasisDirection(_ONE_DIRECTION),
)
model = craft_graph_to_model.craft_graph_to_model(extracted.graph,
extracted.sources)
input_space = _make_input_space(vocab, max_seq_len)
output_space = bases.VectorSpaceWithBasis(
extracted.sink[nodes.OUTPUT_BASIS])
for val in vocab:
test_input = _embed_input([val], input_space)
rasp_output = program([val])
expected_output = _embed_output(
output_seq=rasp_output,
output_space=output_space,
categorical_output=True)
test_output = model.apply(test_input).project(output_space)
self.assertVectorAllClose(
tests_common.strip_bos_token(test_output), expected_output)
@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
categorical_output = rasp.is_categorical(program)
extracted = rasp_to_graph.extract_rasp_graph(program)
basis_inference.infer_bases(
extracted.graph,
extracted.sink,
vocab,
max_seq_len=max_seq_len,
)
expr_to_craft_graph.add_craft_components_to_rasp_graph(
extracted.graph,
bos_dir=bases.BasisDirection(_BOS_DIRECTION),
one_dir=bases.BasisDirection(_ONE_DIRECTION),
)
model = craft_graph_to_model.craft_graph_to_model(extracted.graph,
extracted.sources)
input_space = _make_input_space(vocab, max_seq_len)
output_space = bases.VectorSpaceWithBasis(
extracted.sink[nodes.OUTPUT_BASIS])
if not categorical_output:
self.assertLen(output_space.basis, 1)
test_input_vector = _embed_input(test_input, input_space)
expected_output_vector = _embed_output(
output_seq=expected_output,
output_space=output_space,
categorical_output=categorical_output)
test_output = model.apply(test_input_vector).project(output_space)
self.assertVectorAllClose(
tests_common.strip_bos_token(test_output), expected_output_vector)
@unittest.expectedFailure
def test_setting_default_values_can_lead_to_wrong_outputs_in_compiled_model(
self, program):
# This is an example program in which setting a default value for aggregate
# writes a value to the bos token position, which interfers with a later
# aggregate operation causing the compiled model to have the wrong output.
vocab = {"a", "b"}
test_input = ["a"]
max_seq_len = 2
# RASP: [False, True]
# compiled: [False, False, True]
not_a = rasp.Map(lambda x: x != "a", rasp.tokens)
# RASP:
# [[True, False],
# [False, False]]
# compiled:
# [[False,True, False],
# [True, False, False]]
sel1 = rasp.Select(rasp.tokens, rasp.tokens,
lambda k, q: k == "a" and q == "a")
# RASP: [False, True]
# compiled: [True, False, True]
agg1 = rasp.Aggregate(sel1, not_a, default=True)
# RASP:
# [[False, True]
# [True, True]]
# compiled:
# [[True, False, False]
# [True, False, False]]
# because pre-softmax we get
# [[1.5, 1, 1]
# [1.5, 1, 1]]
# instead of
# [[0.5, 1, 1]
# [0.5, 1, 1]]
# Because agg1 = True is stored on the BOS token position
sel2 = rasp.Select(agg1, agg1, lambda k, q: k or q)
# RASP: [1, 0.5]
# compiled
# [1, 1, 1]
program = rasp.numerical(
rasp.Aggregate(sel2, rasp.numerical(not_a), default=1))
expected_output = [1, 0.5]
# RASP program gives the correct output
program_output = program(test_input)
np.testing.assert_allclose(program_output, expected_output)
extracted = rasp_to_graph.extract_rasp_graph(program)
basis_inference.infer_bases(
extracted.graph,
extracted.sink,
vocab,
max_seq_len=max_seq_len,
)
expr_to_craft_graph.add_craft_components_to_rasp_graph(
extracted.graph,
bos_dir=bases.BasisDirection(_BOS_DIRECTION),
one_dir=bases.BasisDirection(_ONE_DIRECTION),
)
model = craft_graph_to_model.craft_graph_to_model(extracted.graph,
extracted.sources)
input_space = _make_input_space(vocab, max_seq_len)
output_space = bases.VectorSpaceWithBasis(
extracted.sink[nodes.OUTPUT_BASIS])
test_input_vector = _embed_input(test_input, input_space)
expected_output_vector = _embed_output(
output_seq=expected_output,
output_space=output_space,
categorical_output=True)
compiled_model_output = model.apply(test_input_vector).project(output_space)
# Compiled craft model gives correct output
self.assertVectorAllClose(
tests_common.strip_bos_token(compiled_model_output),
expected_output_vector)
if __name__ == "__main__":
absltest.main()
|