128 lines
7.1 KiB
Python
128 lines
7.1 KiB
Python
# Copyright 2025 The HuggingFace Inc. team.
|
|
#
|
|
# 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.
|
|
|
|
import builtins
|
|
import io
|
|
import re
|
|
import unittest
|
|
|
|
from transformers.testing_utils import require_read_token, require_torch
|
|
from transformers.utils.attention_visualizer import AttentionMaskVisualizer
|
|
|
|
|
|
ANSI_RE = re.compile(r"\x1b\[[0-9;]*m")
|
|
|
|
|
|
def _normalize(s: str) -> str:
|
|
# drop ANSI (colors may be disabled on CI), normalize line endings,
|
|
# and strip trailing spaces without touching alignment inside lines
|
|
s = ANSI_RE.sub("", s)
|
|
s = s.replace("\r\n", "\n").replace("\r", "\n")
|
|
return "\n".join(line.rstrip() for line in s.split("\n")).strip()
|
|
|
|
|
|
@require_torch
|
|
class AttentionMaskVisualizerTester(unittest.TestCase):
|
|
"""Test suite for AttentionMaskVisualizer"""
|
|
|
|
@require_read_token
|
|
def test_paligemma_multimodal_visualization(self):
|
|
"""Test AttentionMaskVisualizer with PaliGemma multimodal model"""
|
|
model_name = "hf-internal-testing/namespace_google_repo_name_paligemma-3b-pt-224"
|
|
input_text = "<img> What is in this image?"
|
|
|
|
buf = io.StringIO()
|
|
orig_print = builtins.print
|
|
|
|
def _print(*args, **kwargs):
|
|
kwargs.setdefault("file", buf)
|
|
orig_print(*args, **kwargs)
|
|
|
|
try:
|
|
builtins.print = _print
|
|
visualizer = AttentionMaskVisualizer(model_name)
|
|
visualizer(input_text)
|
|
finally:
|
|
builtins.print = orig_print
|
|
output = buf.getvalue()
|
|
|
|
expected_output = """
|
|
##########################################################################################################################################################################################################################################
|
|
## Attention visualization for \033[1mpaligemma:hf-internal-testing/namespace_google_repo_name_paligemma-3b-pt-224\033[0m PaliGemmaModel ##
|
|
##########################################################################################################################################################################################################################################
|
|
\033[92m■\033[0m: i == j (diagonal) \033[93m■\033[0m: token_type_ids
|
|
Attention Matrix
|
|
|
|
|
|
\033[93m'<image>'\033[0m: 0 \033[93m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
\033[93m'<image>'\033[0m: 1 \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
\033[93m'<image>'\033[0m: 2 \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
\033[93m'<image>'\033[0m: 3 \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
\033[93m'<image>'\033[0m: 4 \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m \033[93m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
'<bos>' : 5 ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
'▁What' : 6 ■ ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
'▁is' : 7 ■ ■ ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
'▁in' : 8 ■ ■ ■ ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
'▁this' : 9 ■ ■ ■ ■ ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ |
|
|
'▁image' : 10 ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ |
|
|
'?' : 11 ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ |
|
|
'\\n' : 12 ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ |
|
|
'<eos>' : 13 ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ \033[92m■\033[0m |
|
|
##########################################################################################################################################################################################################################################
|
|
""" # noqa
|
|
|
|
self.assertEqual(_normalize(output), _normalize(expected_output))
|
|
|
|
@require_read_token
|
|
def test_llama_text_only_visualization(self):
|
|
"""Test AttentionMaskVisualizer with Llama text-only model"""
|
|
model_name = "hf-internal-testing/namespace_meta-llama_repo_name_Llama-2-7b-hf"
|
|
input_text = "Plants create energy through a process known as"
|
|
|
|
buf = io.StringIO()
|
|
orig_print = builtins.print
|
|
|
|
def _print(*args, **kwargs):
|
|
kwargs.setdefault("file", buf)
|
|
orig_print(*args, **kwargs)
|
|
|
|
try:
|
|
builtins.print = _print
|
|
visualizer = AttentionMaskVisualizer(model_name)
|
|
visualizer(input_text)
|
|
finally:
|
|
builtins.print = orig_print
|
|
output = buf.getvalue()
|
|
|
|
expected_output = """
|
|
##########################################################################################################################################################################################################
|
|
## Attention visualization for \033[1mllama:hf-internal-testing/namespace_meta-llama_repo_name_Llama-2-7b-hf\033[0m LlamaModel ##
|
|
##########################################################################################################################################################################################################
|
|
\033[92m■\033[0m: i == j (diagonal) \033[93m■\033[0m: token_type_ids
|
|
Attention Matrix
|
|
|
|
'▁Pl' : 0 \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
'ants' : 1 ■ \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
'▁create' : 2 ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
'▁energy' : 3 ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ ⬚ |
|
|
'▁through': 4 ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ ⬚ |
|
|
'▁a' : 5 ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ ⬚ |
|
|
'▁process': 6 ■ ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ ⬚ |
|
|
'▁known' : 7 ■ ■ ■ ■ ■ ■ ■ \033[92m■\033[0m ⬚ |
|
|
'▁as' : 8 ■ ■ ■ ■ ■ ■ ■ ■ \033[92m■\033[0m |
|
|
##########################################################################################################################################################################################################
|
|
""" # noqa
|
|
|
|
self.assertEqual(_normalize(output), _normalize(expected_output))
|