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# coding=utf-8
# 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 gc
import json
import os
import tempfile
import unittest
from pathlib import Path
from transformers import is_torch_available
from transformers.model_debugging_utils import model_addition_debugger_context
if is_torch_available():
import torch
from torch import nn
class ToyModel(nn.Module):
def __init__(self):
super().__init__()
self.embed = nn.Embedding(10, 4)
self.linear_1 = nn.Linear(4, 8)
self.linear_2 = nn.Linear(8, 2)
self.act = nn.ReLU()
def forward(self, input_ids: str):
hidden_states = self.embed(input_ids).mean(dim=1)
hidden_states = self.act(self.linear_1(hidden_states))
return self.linear_2(hidden_states)
class TestModelAdditionDebugger(unittest.TestCase):
def setUp(self):
self.model = ToyModel()
self.inputs = {"input_ids": torch.randint(0, 10, (1, 3))}
def tearDown(self):
gc.collect()
def test_debugger_outputs(self):
with tempfile.TemporaryDirectory() as tmpdir:
with model_addition_debugger_context(self.model, debug_path=str(tmpdir)):
_ = self.model.forward(**self.inputs)
base = f"{self.model.__class__.__name__}_debug_tree"
summary = Path(os.path.join(tmpdir, f"{base}_SUMMARY.json"))
full = Path(os.path.join(tmpdir, f"{base}_FULL_TENSORS.json"))
self.assertTrue(os.path.isfile(summary) and os.path.isfile(full))
data = json.loads(summary.read_text())
self.assertTrue({"module_path", "inputs", "children"} <= data.keys())
self.assertTrue(data["children"])
class ToyLayer(nn.Module):
def __init__(self, layer_index):
super().__init__()
self.layer_index = layer_index
self.layer_operation = nn.Linear(4, 4)
def forward(self, hidden_states):
return self.layer_operation(hidden_states)
class ToyModelWithLayers(nn.Module):
def __init__(self):
super().__init__()
self.input_proj = nn.Linear(4, 4)
self.layers = nn.ModuleList([ToyLayer(layer_index) for layer_index in range(6)])
self.output_proj = nn.Linear(4, 2)
def forward(self, x):
x = self.input_proj(x)
for layer in self.layers:
x = layer(x)
return self.output_proj(x)
class TestModelWithLayers(unittest.TestCase):
def setUp(self):
self.inputs = {"input_ids": torch.randint(0, 10, (1, 3))}
self.model_with_layers = ToyModelWithLayers()
self.dense_input = {"x": torch.randn(1, 4)}
def tearDown(self):
gc.collect()
def test_layer_pruning_behavior(self):
# No pruning: expect all 6 layers
with tempfile.TemporaryDirectory() as tmpdir:
with model_addition_debugger_context(self.model_with_layers, debug_path=tmpdir, do_prune_layers=False):
_ = self.model_with_layers(**self.dense_input)
summary_path = os.path.join(tmpdir, "ToyModelWithLayers_debug_tree_SUMMARY.json")
with open(summary_path) as f:
data = json.load(f)
self.assertEqual(set(data.keys()), {"module_path", "inputs", "children"})
for layer_index in range(6):
self.assertEqual(
data["children"][layer_index + 1]["module_path"],
f"ToyModelWithLayers.layers.{int(layer_index)}",
)
# Pruning: expect only 2 layers (0 and 5)
with tempfile.TemporaryDirectory() as tmpdir:
with model_addition_debugger_context(self.model_with_layers, debug_path=tmpdir, do_prune_layers=True):
_ = self.model_with_layers(**self.dense_input)
summary_path = os.path.join(tmpdir, "ToyModelWithLayers_debug_tree_SUMMARY.json")
with open(summary_path) as f:
data = json.load(f)
self.assertEqual(set(data.keys()), {"module_path", "inputs", "children"})
self.assertEqual(data["children"][1]["module_path"], "ToyModelWithLayers.layers.0")
self.assertEqual(data["children"][2]["module_path"], "ToyModelWithLayers.layers.5")
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