[ci]: cover transformers 5.x config compat for Qwen2.5-VL (#1576)

transformers 5.x stopped forwarding text-model attributes from the
composite Qwen2_5_VLConfig, turned rope_scaling into a property backed by
rope_parameters, dropped "default" from ROPE_INIT_FUNCTIONS and moved
rope_theta inside rope_parameters. The compat layer absorbing all of this
landed incidentally in 02e1143f (#1471) and has no test coverage, while
#1431 unpinned transformers to >=4.57.3 -- so fresh installs now resolve
to 5.13.0 and every new user is on the v5 path.

Pin both the config normalization and the resulting RoPE numerics.
CPU-only and weight-free: the config is built inline, not downloaded.
Also covers the reason1.py fallback config, which carries no rope_scaling
and takes a different branch on each transformers line.

Verified passing on transformers 4.57.3, 5.12.1 and 5.13.0. Neutering
_flatten_text_config to a no-op fails the suite on 5.x, reproducing the
AttributeErrors reported in #1576.

fastvideo/tests/encoders/test_qwen2_5_vl_config_compat.py

@@ -0,0 +1,125 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Config-compat regression tests for the custom Qwen2.5-VL encoder.
+
+transformers 5.x stopped forwarding text-model attributes (``vocab_size``,
+``pad_token_id``, ``hidden_size``, ...) from the composite ``Qwen2_5_VLConfig``
+down from ``text_config``, turned ``rope_scaling`` into a property backed by
+``rope_parameters``, dropped the ``"default"`` entry from ``ROPE_INIT_FUNCTIONS``
+and moved ``rope_theta`` inside ``rope_parameters``. See issue #1576.
+
+``qwen2_5_vl_custom`` absorbs all of that. These tests pin both the config
+normalization and the resulting RoPE numerics, so the compat layer cannot
+silently regress as transformers evolves. They are CPU-only and weight-free:
+the config is built inline rather than downloaded.
+"""
+import torch
+from transformers import Qwen2_5_VLConfig
+
+from fastvideo.models.encoders.qwen2_5_vl_custom import (
+    Qwen2_5_VLRotaryEmbedding,
+    _compute_default_rope_parameters,
+    _flatten_text_config,
+)
+
+# Shape of Qwen/Qwen2.5-VL-7B-Instruct, the checkpoint Cosmos 2.5's Reason1 text encoder is built on.
+HIDDEN_SIZE = 3584
+NUM_ATTENTION_HEADS = 28
+VOCAB_SIZE = 152064
+ROPE_THETA = 1000000.0
+MROPE_SECTION = [16, 24, 24]
+HEAD_DIM = HIDDEN_SIZE // NUM_ATTENTION_HEADS  # 128
+
+
+def _build_config() -> Qwen2_5_VLConfig:
+    # Flat, 4.x-style kwargs: this is what a real checkpoint's config.json carries.
+    return Qwen2_5_VLConfig(
+        hidden_size=HIDDEN_SIZE,
+        intermediate_size=18944,
+        num_attention_heads=NUM_ATTENTION_HEADS,
+        num_hidden_layers=28,
+        num_key_value_heads=4,
+        vocab_size=VOCAB_SIZE,
+        max_position_embeddings=128000,
+        rope_theta=ROPE_THETA,
+        rope_scaling={"type": "mrope", "mrope_section": MROPE_SECTION},
+        tie_word_embeddings=False,
+    )
+
+
+def _expected_inv_freq() -> torch.Tensor:
+    return 1.0 / (ROPE_THETA**(torch.arange(0, HEAD_DIM, 2, dtype=torch.int64).float() / HEAD_DIM))
+
+
+def test_flatten_text_config_exposes_text_attrs_on_composite_config() -> None:
+    """Attributes the module reads off the top-level config must resolve after flattening."""
+    config = _flatten_text_config(_build_config())
+
+    assert config.vocab_size == VOCAB_SIZE
+    assert config.hidden_size == HIDDEN_SIZE
+    assert config.num_attention_heads == NUM_ATTENTION_HEADS
+    assert config.max_position_embeddings == 128000
+    # Present but legitimately None for this checkpoint; the module reads it via getattr.
+    assert hasattr(config, "pad_token_id")
+
+
+def test_flatten_text_config_preserves_mrope_settings() -> None:
+    """``rope_scaling`` must survive the 4.x -> 5.x ``rope_parameters`` rename, mrope section intact."""
+    config = _flatten_text_config(_build_config())
+
+    rope_scaling = config.rope_scaling
+    assert rope_scaling is not None
+    assert rope_scaling["mrope_section"] == MROPE_SECTION
+
+    # The "rope_type" vs legacy "type" lookup performed by Qwen2_5_VLRotaryEmbedding.
+    # Both transformers lines normalize an "mrope" request to "default" before we see it:
+    # 4.x rewrites rope_scaling["type"] in place, 5.x sets rope_parameters["rope_type"].
+    # Asserting "mrope" here would fail on every supported version.
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type"))
+    assert rope_type == "default"
+
+
+def test_compute_default_rope_parameters_reads_theta_on_any_transformers() -> None:
+    """``rope_theta`` moved inside ``rope_parameters`` in 5.x; the helper must find it either way."""
+    config = _flatten_text_config(_build_config())
+
+    inv_freq, attention_scaling = _compute_default_rope_parameters(config)
+
+    assert attention_scaling == 1.0
+    assert inv_freq.shape == (HEAD_DIM // 2, )
+    torch.testing.assert_close(inv_freq, _expected_inv_freq())
+
+
+def test_rotary_embedding_handles_config_without_rope_scaling() -> None:
+    """``reason1.py`` falls back to a config carrying no ``rope_scaling``; it must still build.
+
+    This exercises a different branch on each line: on 4.x ``rope_scaling`` is ``None`` and
+    ``Qwen2_5_VLRotaryEmbedding`` takes its ``else``; on 5.x ``rope_parameters`` is populated
+    with a bare ``rope_theta``/``rope_type`` pair. Both must land on the default init fn.
+    """
+    config = _flatten_text_config(
+        Qwen2_5_VLConfig(
+            hidden_size=HIDDEN_SIZE,
+            intermediate_size=18944,
+            max_window_layers=28,
+            num_attention_heads=NUM_ATTENTION_HEADS,
+            num_hidden_layers=28,
+            num_key_value_heads=4,
+            tie_word_embeddings=False,
+            vocab_size=VOCAB_SIZE,
+        ))
+
+    rotary_embedding = Qwen2_5_VLRotaryEmbedding(config)
+
+    assert rotary_embedding.rope_type == "default"
+    assert rotary_embedding.inv_freq.shape == (HEAD_DIM // 2, )
+
+
+def test_rotary_embedding_builds_from_flattened_config() -> None:
+    """End-to-end: the rotary embedding must pick a valid init fn and produce 4.x-identical numerics."""
+    config = _flatten_text_config(_build_config())
+
+    rotary_embedding = Qwen2_5_VLRotaryEmbedding(config)
+
+    assert rotary_embedding.rope_type == "default"
+    assert rotary_embedding.attention_scaling == 1.0
+    torch.testing.assert_close(rotary_embedding.inv_freq, _expected_inv_freq())