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[misc] fix qwen35 tests: correct the text model type and skip reverse_mapping #45173

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Merged
Rocketknight1 merged 2 commits into from
Apr 2, 2026
Merged

[misc] fix qwen35 tests: correct the text model type and skip reverse_mapping #45173

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5259b2a
tests: skip qwen3.5 reverse mapping for vlm
JJJYmmm Apr 1, 2026
9fa91af
update
JJJYmmm Apr 1, 2026
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8 changes: 7 additions & 1 deletion tests/models/qwen3_5/test_modeling_qwen3_5.py
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Original file line number Diff line number Diff line change
Expand Up @@ -162,7 +162,7 @@ def __init__(
"vocab_size": 99,
"intermediate_size": 37,
"max_position_embeddings": 512,
"model_type": "qwen3_vl",
"model_type": "qwen3_5_text",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"layer_types": ["full_attention", "linear_attention"],
Expand Down Expand Up @@ -304,6 +304,12 @@ def setUp(self):
def test_config(self):
self.config_tester.run_common_tests()

@unittest.skip(
"Conversion only for the `CausalLM` loading from saved `ConditionalLM`, doesn't apply to simple VLM"
)
def test_reverse_loading_mapping(self, check_keys_were_modified=True):
pass

def _get_conv_state_shape(self, batch_size: int, config):
num_v_heads = config.linear_num_value_heads
num_k_heads = config.linear_num_key_heads
Expand Down
98 changes: 9 additions & 89 deletions tests/models/qwen3_5_moe/test_modeling_qwen3_5_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,16 +14,9 @@
"""Testing suite for the PyTorch Qwen3.5 model."""

import copy
import os
import re
import tempfile
import unittest

from safetensors.torch import load_file

from transformers import is_torch_available
from transformers.conversion_mapping import get_model_conversion_mapping
from transformers.core_model_loading import WeightRenaming, process_target_pattern
from transformers.testing_utils import (
require_torch,
torch_device,
Expand All @@ -34,7 +27,6 @@
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
compare_state_dicts,
floats_tensor,
ids_tensor,
)
Expand Down Expand Up @@ -137,87 +129,9 @@ def test_attention_outputs(self):
self.assertEqual(len(self_attentions), sum(layer == "full_attention" for layer in config.layer_types))
self.assertListEqual(list(self_attentions[0].shape[-3:]), [config.num_attention_heads, seq_len, seq_len])

@unittest.skip("Intentionally not reversable (no changes) as only load time within a VLM depends on this")
def test_reverse_loading_mapping(self, check_keys_were_modified=True):
"""
Overwritten to check for the moe portion but ignore the prefix as it results into a noop
(except we have a VLM struct initially)
"""
config, _ = self.model_tester.prepare_config_and_inputs_for_common()

# Some MoE models alternate between a classic MLP and a MoE layer, in which case we want to have at
# lest one MoE layer here to check the mapping
config_to_set = config.get_text_config(decoder=True)
config_to_set.first_k_dense_replace = 1 # means that the first layer (idx 0) will be MLP, then MoE
config_to_set.moe_layer_start_index = 1 # same as above but for Ernie 4.5...
config_to_set.mlp_only_layers = [0] # same but for qwens
config_to_set.num_dense_layers = 1 # lfm2_moe

for model_class in self.all_model_classes:
# Each individual model is a subtest
with self.subTest(model_class.__name__):
model = model_class(copy.deepcopy(config))
# Skip if no conversions
conversions = get_model_conversion_mapping(model, add_legacy=False)
if len(conversions) == 0:
self.skipTest("No conversion found for this model")

# Find the model keys, so the targets according to the conversions
model_keys = list(model.state_dict().keys())

with tempfile.TemporaryDirectory() as tmpdirname:
# Serialize with reverse mapping
model.save_pretrained(tmpdirname)
state_dict = load_file(os.path.join(tmpdirname, "model.safetensors"))
# Get all the serialized keys that we just saved according to the reverse mapping
serialized_keys = list(state_dict.keys())

if check_keys_were_modified:
# They should be different, otherwise we did not perform any mapping
self.assertNotEqual(sorted(serialized_keys), sorted(model_keys), "No key mapping was performed!")

# Check that for each conversion entry, we at least map to one key
for conversion in conversions:
for source_pattern in conversion.source_patterns:
# Sometimes the mappings specify keys that are tied, so absent from the saved state dict
if isinstance(conversion, WeightRenaming):
# We need to revert the target pattern to make it compatible with regex search
target_pattern_reversed = conversion.target_patterns[0]
captured_group = process_target_pattern(source_pattern)[1]
if captured_group:
target_pattern_reversed = target_pattern_reversed.replace(r"\1", captured_group)
if any(re.search(target_pattern_reversed, k) for k in model.all_tied_weights_keys.keys()):
continue
num_matches = sum(re.search(source_pattern, key) is not None for key in serialized_keys)

# Key change: special case to load causal lm within vlm
if source_pattern == "^model.language_model":
continue

self.assertTrue(
num_matches > 0,
f"`{source_pattern}` in `{conversion}` did not match any of the source keys. "
"This indicates whether that the pattern is not properly written, ot that it could not be reversed correctly",
)

# If everything is still good at this point, let's test that we perform the same operations both when
# reverting ops from `from_pretrained` and from `__init__`
with tempfile.TemporaryDirectory() as tmpdirname:
# The model was instantiated from __init__ before being saved
model.save_pretrained(tmpdirname)
state_dict_saved_from_init = load_file(os.path.join(tmpdirname, "model.safetensors"))

# Now reload it
model_reloaded = model_class.from_pretrained(tmpdirname)

# Make sure both loaded state_dict are identical
self.assertTrue(compare_state_dicts(model_reloaded.state_dict(), model.state_dict()))

# The model was instantiated from `from_pretrained` before being saved
model_reloaded.save_pretrained(tmpdirname)
state_dict_saved_from_pretrained = load_file(os.path.join(tmpdirname, "model.safetensors"))

# Make sure both saved state_dict are identical
self.assertTrue(compare_state_dicts(state_dict_saved_from_init, state_dict_saved_from_pretrained))
pass

@unittest.skip("The specific cache format cannot be instantiated from dp/ddp data.")
def test_multi_gpu_data_parallel_forward(self):
Expand All @@ -243,7 +157,7 @@ def __init__(
"vocab_size": 99,
"intermediate_size": 37,
"max_position_embeddings": 512,
"model_type": "qwen3_vl",
"model_type": "qwen3_5_moe_text",
"num_attention_heads": 4,
"num_hidden_layers": 2,
"layer_types": ["full_attention", "linear_attention"],
Expand Down Expand Up @@ -386,6 +300,12 @@ def setUp(self):
def test_config(self):
self.config_tester.run_common_tests()

@unittest.skip(
"Conversion only for the `CausalLM` loading from saved `ConditionalLM`, doesn't apply to simple VLM"
)
def test_reverse_loading_mapping(self, check_keys_were_modified=True):
pass

def _get_conv_state_shape(self, batch_size: int, config):
num_v_heads = config.linear_num_value_heads
num_k_heads = config.linear_num_key_heads
Expand Down
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