mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 12:50:06 +06:00
4afd3f4820
* debugging improvements
* add debugging details
* add more debugging details
* debug more
* clean up layers + output
* add summary json file
* cleanup
* copies 👀
* remove hooks + add documentation
* draft a small test, why not
* respect the format (respect it)
* fixup imports
* nit
* add tests and configurable pruning of layers
123 lines
4.9 KiB
Python
123 lines
4.9 KiB
Python
# coding=utf-8
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# Copyright 2025 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import json
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import os
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import tempfile
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import unittest
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from pathlib import Path
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from transformers import is_torch_available
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from transformers.model_debugging_utils import model_addition_debugger_context
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if is_torch_available():
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import torch
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from torch import nn
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class ToyModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.embed = nn.Embedding(10, 4)
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self.linear_1 = nn.Linear(4, 8)
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self.linear_2 = nn.Linear(8, 2)
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self.act = nn.ReLU()
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def forward(self, input_ids: str):
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hidden_states = self.embed(input_ids).mean(dim=1)
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hidden_states = self.act(self.linear_1(hidden_states))
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return self.linear_2(hidden_states)
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class TestModelAdditionDebugger(unittest.TestCase):
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def setUp(self):
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self.model = ToyModel()
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self.inputs = {"input_ids": torch.randint(0, 10, (1, 3))}
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def tearDown(self):
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gc.collect()
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def test_debugger_outputs(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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with model_addition_debugger_context(self.model, debug_path=str(tmpdir)):
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_ = self.model.forward(**self.inputs)
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base = f"{self.model.__class__.__name__}_debug_tree"
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summary = Path(os.path.join(tmpdir, f"{base}_SUMMARY.json"))
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full = Path(os.path.join(tmpdir, f"{base}_FULL_TENSORS.json"))
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self.assertTrue(os.path.isfile(summary) and os.path.isfile(full))
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data = json.loads(summary.read_text())
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self.assertTrue({"module_path", "inputs", "children"} <= data.keys())
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self.assertTrue(data["children"])
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class ToyLayer(nn.Module):
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def __init__(self, layer_index):
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super().__init__()
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self.layer_index = layer_index
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self.layer_operation = nn.Linear(4, 4)
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def forward(self, hidden_states):
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return self.layer_operation(hidden_states)
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class ToyModelWithLayers(nn.Module):
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def __init__(self):
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super().__init__()
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self.input_proj = nn.Linear(4, 4)
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self.layers = nn.ModuleList([ToyLayer(layer_index) for layer_index in range(6)])
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self.output_proj = nn.Linear(4, 2)
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def forward(self, x):
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x = self.input_proj(x)
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for layer in self.layers:
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x = layer(x)
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return self.output_proj(x)
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class TestModelWithLayers(unittest.TestCase):
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def setUp(self):
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self.inputs = {"input_ids": torch.randint(0, 10, (1, 3))}
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self.model_with_layers = ToyModelWithLayers()
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self.dense_input = {"x": torch.randn(1, 4)}
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def tearDown(self):
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gc.collect()
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def test_layer_pruning_behavior(self):
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# No pruning: expect all 6 layers
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with tempfile.TemporaryDirectory() as tmpdir:
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with model_addition_debugger_context(self.model_with_layers, debug_path=tmpdir, do_prune_layers=False):
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_ = self.model_with_layers(**self.dense_input)
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summary_path = os.path.join(tmpdir, "ToyModelWithLayers_debug_tree_SUMMARY.json")
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with open(summary_path) as f:
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data = json.load(f)
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self.assertEqual(set(data.keys()), {"module_path", "inputs", "children"})
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for layer_index in range(6):
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self.assertEqual(
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data["children"][layer_index + 1]["module_path"],
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f"ToyModelWithLayers.layers.{int(layer_index)}",
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)
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# Pruning: expect only 2 layers (0 and 5)
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with tempfile.TemporaryDirectory() as tmpdir:
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with model_addition_debugger_context(self.model_with_layers, debug_path=tmpdir, do_prune_layers=True):
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_ = self.model_with_layers(**self.dense_input)
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summary_path = os.path.join(tmpdir, "ToyModelWithLayers_debug_tree_SUMMARY.json")
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with open(summary_path) as f:
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data = json.load(f)
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self.assertEqual(set(data.keys()), {"module_path", "inputs", "children"})
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self.assertEqual(data["children"][1]["module_path"], "ToyModelWithLayers.layers.0")
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self.assertEqual(data["children"][2]["module_path"], "ToyModelWithLayers.layers.5")
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