Add Granite 4.1 Vision (granite4_vision) by artem-spector · Pull Request #45597 · huggingface/transformers

artem-spector · 2026-04-23T06:51:56Z

What does this PR do?

Adds built-in support for Granite 4.1 Vision (granite4_vision), IBM's multimodal vision-language model for enterprise document understanding.

Architecture highlights

Vision encoder: SigLIP2 (google/siglip2-so400m-patch16-384), tiled 384×384 patches
Window Q-Former projector: 4×4 patch windows compressed to 2×2 query tokens via cross-attention (downsample_rate="4/8")
DeepStack feature injection: 8 vision-to-LLM injection points across two mechanisms:
- LayerDeepstack: features from 4 vision encoder depths injected at 4 LLM layers (reversed order — deepest vision → earliest LLM)
- SpatialDeepstack: deepest features split into 4 spatial offset groups (TL/TR/BL/BR), injected at 4 later LLM layers
Language model: GraniteForCausalLM (3.5B) with a rank-256 LoRA adapter (same-repo, LM-only)

Files added

File	Purpose
`modular_granite4_vision.py`	Source of truth — inherits from LLaVA-Next, overrides novel components
`configuration_granite4_vision.py`	Config (generated)
`modeling_granite4_vision.py`	Model (generated)
`processing_granite4_vision.py`	Unified processor (generated)
`image_processing_granite4_vision.py`	Torchvision-based image processor
`image_processing_pil_granite4_vision.py`	PIL/NumPy image processor
`tests/models/granite4_vision/`	Modeling, image processing, and processor tests
`docs/source/en/model_doc/granite4_vision.md`	Model documentation

Auto-registration

Config: auto-generated via configuration_granite4_vision.py model_type
Modeling: MODEL_MAPPING_NAMES + MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES
Processing + image processing: registered in respective auto files

Tests

Unit tests pass locally (pytest tests/models/granite4_vision/ -x -q)
@slow integration tests load real checkpoint and assert outputs within tolerance
make style and make check-repo pass (3 remaining failures are pre-existing upstream issues: mlinter version mismatch and Sam3Lite incomplete model)

Before submitting

This PR is not a duplicate
I have read the contributor guidelines
The documentation reflects the changes
The tests pass

+## Notes
+
+- The model includes LoRA adapters. Call `model.merge_lora_adapters()` after loading to merge them into base weights for faster inference.
+
+- Set `padding_side="left"` during batched generation for more accurate results.
+
+```py
+processor.tokenizer.padding_side = "left"
+```
+
+- The model supports specialized task tags for document extraction: `<chart2csv>`, `<chart2summary>`, `<chart2code>`, `<tables_html>`, `<tables_otsl>`, `<tables_json>`. Pass these as the text prompt along with a document image.
+
+- For key-value pair extraction, provide a JSON schema describing the fields to extract. The model returns structured JSON matching the schema.


lets move this block as Usage Tips section, before the usage example code snippets

Done — moved to a "Usage Tips" section before the code examples.

zucchini-nlp · 2026-04-27T10:05:56Z

@@ -0,0 +1,155 @@
+import math


following "one-model - one-file" philosophy, it is better put inside modular/modeling files

Done — downsampling_granite4_vision.py deleted, all contents inlined into the modular.

zucchini-nlp · 2026-04-27T10:54:26Z

+    "openai-privacy-filter": "OpenAIPrivacyFilterConfig",
    "lasr": "LasrCTCConfig",
    "wav2vec2-with-lm": "Wav2Vec2Config",
+    "granite4-vision": "Granite4VisionConfig",
+    "hy-v3": "HYV3Config",
+    "slanet": "SLANetConfig",
 }


a few bad rebases :)

Done — removed the stale entries introduced by bad rebases.

zucchini-nlp · 2026-04-27T10:55:03Z

+            WeightRenaming(
+                source_patterns=r"(vision_tower\.)vision_model\.",
+                target_patterns=r"\1",
+            ),


I think it is not needed anymore, we added PrefixWeights recently and fixed all llava models

Done — removed the granite4_vision entry from conversion_mapping.py.

zucchini-nlp · 2026-04-27T10:55:11Z

@@ -0,0 +1,253 @@
+# Copyright 2025 IBM. All rights reserved.


zucchini-nlp · 2026-04-27T10:55:51Z

+class Granite4VisionModelTester(VLMModelTester):
+    base_model_class = Granite4VisionModel
+    config_class = Granite4VisionConfig
+    conditional_generation_class = Granite4VisionForConditionalGeneration
+    text_config_class = GraniteConfig
+    vision_config_class = CLIPVisionConfig
+
+    def __init__(self, parent, **kwargs):


we need only this tester, since processing is identical to llava-next. Thanks for using VLMTester 🤩

Done — removed test_image_processing_granite4_vision.py entirely (processing is identical to LlavaNext, no re-definition needed).

zucchini-nlp · 2026-04-27T10:56:20Z

+    "granite4_vision",
    "falcon3",
    "megatron_gpt2",
    "code_llama",
+    "hy_v3",
+    "openai_privacy_filter",
+    "slanet",


also bad rebase

artem-spector · 2026-04-29T04:33:19Z

@zucchini-nlp I'm ready for the second round :)

zucchini-nlp

Thanks @artem-spector ! Great work, glad to see a singlr modular file.

Left some comments on further cleaning-up, and a few questions. I just noticed that all image placeholders are filled with zeros, and not sure if that is intended. If we actually have no image features scattered, can we stop adding that many placeholders without quality degradation? Dummy and unnecessary token ids increase total length and looks like a waste of resources

zucchini-nlp · 2026-04-29T10:13:57Z

+model_id = "ibm-granite/granite-vision-4.1-4b"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(model_id).eval()


nit: [...], device_map="auto") without eval, shoudl be already in eval mode when loaded

Done — removed .eval().

zucchini-nlp · 2026-04-29T10:14:54Z

        ("glm_image", {"pil": "GlmImageImageProcessorPil", "torchvision": "GlmImageImageProcessor"}),
        ("glpn", {"pil": "GLPNImageProcessorPil", "torchvision": "GLPNImageProcessor"}),
        ("got_ocr2", {"pil": "GotOcr2ImageProcessorPil", "torchvision": "GotOcr2ImageProcessor"}),
+        ("granite4_vision", {"pil": "Granite4VisionImageProcessorPil", "torchvision": "Granite4VisionImageProcessor"}),


can delete this, already mapped to 'LlavaNext'

Done — removed.

zucchini-nlp · 2026-04-29T10:17:28Z

@@ -0,0 +1,734 @@
+# Copyright 2025 IBM. All rights reserved.


ig you forgot to commit and push 😄

zucchini-nlp · 2026-04-29T10:19:16Z

+
+
+@dataclass
+class Granite4VisionImageFeaturesOutput(ModelOutput):


i think same as qwen3_vl.BaseModelOutputWithDeepstackFeatures

Done — Granite4VisionImageFeaturesOutput now inherits BaseModelOutputWithPooling (same approach as qwen3_vl.BaseModelOutputWithDeepstackFeatures), with a deepstack_features field added.

zucchini-nlp · 2026-04-29T10:20:09Z

+class Granite4VisionTextConfig(PreTrainedConfig):
+    model_type = "granite4_vision_text"
+    base_config_key = "text_config"


not sure I am following, are we not supposed to inherit from GraniteConfig? Current class has no attributes

Done — Granite4VisionTextConfig inherits GraniteConfig directly. It has no additional attributes because the text config is fully specified by GraniteConfig; the subclass exists only to set model_type = "granite4_vision_text" and base_config_key = "text_config".

zucchini-nlp · 2026-04-29T10:53:21Z

+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            deepstack_features=outputs.deepstack_features,


the only diff from llava-next is returning deepstack_features?

Yes — the main differences from LlavaNextForConditionalGeneration are: (1) the model uses Granite4VisionModel which has deepstack injection, (2) logits are scaled by text_config.logits_scaling, and (3) deepstack_features is threaded through the output. Everything else is inherited.

zucchini-nlp · 2026-04-29T10:55:47Z

+            logits_to_keep=logits_to_keep,
+            **kwargs,
+        )
+        model_inputs = self._init_hybrid_cache(**model_inputs)


we should be able to delete this line to inti cache. Correct cache should be init by generationMixin._prepare_cache_for_generation automatically

Done — removed; GenerationMixin._prepare_cache_for_generation handles cache initialization.

zucchini-nlp · 2026-04-29T10:56:47Z

+    @unittest.skip("Granite4VisionImageFeaturesOutput has no hidden_states field")
+    def test_get_image_features_hidden_states(self):
+        pass
+
+    @unittest.skip("Granite4VisionImageFeaturesOutput has no attentions field")
+    def test_get_image_features_attentions(self):
+        pass


these two should be fixed when we add **kwargs and return a BaseModelOutputWithDeepstackFeatures

Done — Granite4VisionImageFeaturesOutput now inherits BaseModelOutputWithPooling so all five tests pass. skip_test_image_features_output_shape = True remains because last_hidden_state isn't meaningful for this output type, but hidden_states, pooler_output, and field presence checks all pass.

zucchini-nlp · 2026-04-29T10:57:39Z

+    @unittest.skip("Base model forward returns ModelOutputWithPast, not CausalLMOutput with loss")
+    def test_training(self):
+        pass
+
+    @unittest.skip("QFormer submodules not initialized by init_weights from meta device")
+    def test_can_init_all_missing_weights(self):
+        pass


these also should be fixable, I don't think this is a valid reason to skip

Done for test_training — the skip was stale; Granite4VisionForConditionalGeneration computes a loss, and the framework skips Granite4VisionModel automatically via MODEL_MAPPING_NAMES.\n\ntest_can_init_all_missing_weights remains skipped: Blip2QFormerModel submodules aren't initialized from meta device by our _init_weights — Blip2QFormerPreTrainedModel._init_weights only handles Blip2ForConditionalGeneration instances, not the standalone Blip2QFormerModel. Happy to investigate further if you'd like.

zucchini-nlp · 2026-04-29T10:58:36Z

+    "Granite4VisionConfig": [
+        "multimodal_projector_bias",
+        "projector_hidden_act",
+    ],


if actually not used, lets just delete them from config class. When inheriting config, you can define

multimodal_projector_bias = AtributeErro()

and it will not copy this field

Done — multimodal_projector_bias and projector_hidden_act are shadowed with AttributeError() at class level on Granite4VisionConfig, so the framework sees them as intentionally inaccessible. The SPECIAL_CASES_TO_ALLOW entry was removed.

artem-spector · 2026-05-01T08:43:22Z

Hi @zucchini-nlp, sorry for delay - I was chasing a performance problem, which turn out to be not real.

Summary of changes:

Granite4VisionImageFeaturesOutput now inherits BaseModelOutputWithPooling instead of bare ModelOutput, which unblocks the common image-features tests
Added @capture_outputs to Granite4VisionTextModel.forward so output_hidden_states propagates correctly through generation
Removed the manual DynamicCache init from forward — GenerationMixin._prepare_cache_for_generation handles this
get_image_features now properly passes output_hidden_states through (via kwarg or config fallback)
qformer_config is now fully specified at init time rather than patched post-super().init()
_can_record_outputs and _deepstack_inject moved up to Granite4VisionPreTrainedModel
One-letter variable names renamed for clarity; unused inherited config attrs now raise AttributeError with a clear message

Open question — test_can_init_all_missing_weights:

This test verifies that every weight can be re-initialized from meta device via _init_weights. It fails because WindowQFormerDownsampler embeds a Blip2QFormerModel (a full PreTrainedModel from a different family). When from_pretrained(None, state_dict={}) walks submodules, our _init_weights has no isinstance branches for QFormer's internal layers (attention, embeddings, etc.), so they stay uninitialized.
Is there a recommended approach here, or is skipping acceptable for embedded third-party PreTrainedModel submodules?

zucchini-nlp · 2026-05-01T09:15:53Z

When from_pretrained(None, state_dict={}) walks submodules, our _init_weights has no isinstance branches for QFormer's internal layers (attention, embeddings, etc.), so they stay uninitialized.

That is weird, usually our initializer walks down each sub-module and initializes its weights. Unless Qformer has special buffers that need to be init, it should be fine. I can check it on Monday and do another review pass

artem-spector · 2026-05-01T10:44:30Z

When from_pretrained(None, state_dict={}) walks submodules, our _init_weights has no isinstance branches for QFormer's internal layers (attention, embeddings, etc.), so they stay uninitialized.

That is weird, usually our initializer walks down each sub-module and initializes its weights. Unless Qformer has special buffers that need to be init, it should be fine. I can check it on Monday and do another review pass

will re-check too

Full implementation of IBM Granite 4.1 Vision as a built-in HF model: - Modular implementation (modular_granite4_vision.py) - Generated files: config, modeling, image processing, processing - Auto-registration: config, modeling, processing, image processing - Tests: modeling (unit + @slow), image processor, processor - Documentation (docs/source/en/model_doc/granite4_vision.md) - WeightRenaming to handle SiglipVisionModel vision_model. nesting Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Upstream moved CONFIG_MAPPING_NAMES to auto_mappings.py. Add granite4_vision entry there; resolve leftover conflict markers in configuration_auto.py (granite4_vision is already in modeling_auto.py and processing_auto.py). Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

…mappings duplicate Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

- Remove granite4_vision from MISSING_IMAGE_PROCESSOR_MAPPING_NAMES (auto-discovered via TorchvisionBackend/PilBackend) - Add granite4-vision to HARDCODED_CONFIG_FOR_MODELS in auto_docstring.py - Add granite4_vision to DOC_MODEL_NAMES_NOT_IN_AUTO in check_repo.py - Fix import sort in models/__init__.py and test file - Regenerate auto_mappings.py via check_auto.py --fix_and_overwrite - Add dates to granite4_vision.md Co-Authored-By: Claude Sonnet 4.6 <[email protected]> Signed-off-by: artemspector <[email protected]>

Co-Authored-By: Claude Sonnet 4.6 <[email protected]> Signed-off-by: artemspector <[email protected]>

- Fix processing_auto.py sort order (sort_auto_mappings) - Add hy-v3, openai-privacy-filter, slanet to HARDCODED_CONFIG_FOR_MODELS - Add hy_v3, openai_privacy_filter, slanet to DOC_MODEL_NAMES_NOT_IN_AUTO (new upstream models missing from these registries) Co-Authored-By: Claude Sonnet 4.6 <[email protected]> Signed-off-by: artemspector <[email protected]>

sam3_vision_model and sam3_vit_model were incorrectly mapped to Sam3LiteTextVisionConfig/Sam3LiteTextViTConfig instead of Sam3VisionConfig/Sam3ViTConfig (and sam3_lite_text module instead of sam3). These are unrelated to granite4_vision; restoring upstream/main values. Signed-off-by: artemspector <[email protected]>

…ebase regeneration These three upstream model entries were accidentally removed from CONFIG_MAPPING_NAMES in auto_mappings.py by a previous run of check_auto.py --fix_and_overwrite during an incomplete rebase state. Restoring verbatim from upstream/main. Signed-off-by: artemspector <[email protected]>

Signed-off-by: artemspector <[email protected]>

- Item 20: drop get_image_token_mask override, use parent's get_placeholder_mask - Item 29: delete test_image_processing_granite4_vision.py (identical to LlavaNext) Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

- Pass output_attentions/output_hidden_states explicitly to language_model in Granite4VisionModel.forward (were swallowed as explicit params, not forwarded via **kwargs) - Collect all_hidden_states and all_self_attns in Granite4VisionTextModel layer loop; add output_attentions/output_hidden_states params - Fix qformer_config dict→object conversion to run before super().__post_init__() so _attn_implementation.setter doesn't hit a raw dict during sub_configs iteration - Use Blip2QFormerConfig directly in sub_configs (instead of AutoConfig) so save/load round-trip resolves the type correctly; add missing import to generated configuration_granite4_vision.py Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

…es AutoConfig blip_2_qformer is registered in CONFIG_MAPPING so AutoConfig resolves it correctly. Moving the Blip2QFormerConfig import inside __post_init__ avoids a cross-model top-level import that the modular converter drops from the generated file. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

…ing public classes - IGNORE_NON_TESTED + IGNORE_NON_AUTO_CONFIGURED: Granite4VisionTextModel is an internal subcomponent tested implicitly through Granite4VisionModel - Doc: add autodoc entries for Granite4VisionTextConfig, Granite4VisionTextModel, Granite4VisionImageProcessor, Granite4VisionImageProcessorPil Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Needed for ruff F821 (undefined name) to pass under make style. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

…isionModel.forward Aligns with reviewer feedback: these args are not needed in the explicit signature since they flow through kwargs: Unpack[TransformersKwargs]. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

@strict

- TRF010: add @strict to Granite4VisionTextConfig (direct PreTrainedConfig subclass) - TRF002: set base_model_prefix = "model" on Granite4VisionTextModel (was "") - TRF009: add trf-ignore comment on Blip2QFormerModel lazy import (cross-model import is intentional — QFormer is a shared building block) Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

- Reorder imports in modular to satisfy ruff isort (stdlib → third-party → first-party) - Sync processing_granite4_vision.py to match converter output (BatchFeature from feature_extraction_utils, no model_type on processor) Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

downsampling_granite4_vision.py, image_processing_granite4_vision.py, and image_processing_pil_granite4_vision.py are regenerated by the converter but were previously intentionally deleted: image processors delegate to LlavaNext (registered in image_processing_auto.py), and downsampling is inlined in modular/modeling. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

… this model Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

…ctor naming - Granite4VisionTextConfig restored as proper GraniteConfig subclass - Granite4VisionConfig.__post_init__: convert dict->config before super() so _attn_implementation.setter sees config objects; patch vision-size fields after super() - Use CONFIG_MAPPING/AutoModel at module top-level (no lazy imports) - Add _can_record_outputs to Granite4VisionTextModel for hidden_states/attentions capture via @capture_outputs decorator - Add Granite4VisionTextAttention/TextDecoderLayer stubs in modular so converter generates the registry entries pointing to the correct layer classes - WindowQFormerDownsampler renamed to Granite4VisionWindowQFormerDownsampler - interpolate_downsample/spatial_offset_downsample take explicit size args (not config) - Remove output_attentions/output_hidden_states from forward signatures (handled by @capture_outputs and **kwargs); use BaseModelOutputWithPast return type - Remove prepare_inputs_for_generation (handled by parent) - Remove _init_hybrid_cache (GraniteMoeHybrid leftover from 4.0) - auto_mappings.py: use LlavaNextImageProcessor(Pil) instead of model-specific copies - docs: remove .eval() from example Co-Authored-By: Claude Sonnet 4.6 <[email protected]> Signed-off-by: artemspector <[email protected]>

…onfig attrs - _windowed_raster/unwindowed_raster: x -> features, x_win -> windowed_features - __init__: q, w -> query_side_str, window_side_str - Granite4VisionConfig: shadow LlavaNextConfig's multimodal_projector_bias and projector_hidden_act with AttributeError() so check_config_attributes passes without SPECIAL_CASES_TO_ALLOW entry - Remove Granite4VisionConfig from check_config_attributes.py SPECIAL_CASES_TO_ALLOW Co-Authored-By: Claude Sonnet 4.6 <[email protected]> Signed-off-by: artemspector <[email protected]>

…d patching Peek at vision_config.hidden_size (or its dict equivalent) before super() and include hidden_size, num_attention_heads, encoder_hidden_size directly in the CONFIG_MAPPING["blip_2_qformer"]() constructor call. This avoids mutating the config object after super().__post_init__() runs. Co-Authored-By: Claude Sonnet 4.6 <[email protected]> Signed-off-by: artemspector <[email protected]>

…ainedModel Follows qwen3_vl pattern: _can_record_outputs and _deepstack_inject belong on the shared PreTrainedModel base class, not on TextModel. TextAttention/ TextDecoderLayer stubs are defined before PreTrainedModel so the converter generates locally-scoped classes for the _can_record_outputs registry. Co-Authored-By: Claude Sonnet 4.6 <[email protected]> Signed-off-by: artemspector <[email protected]>

…cCache - Granite4VisionImageFeaturesOutput now inherits BaseModelOutputWithPooling so the common test framework can introspect last_hidden_state/pooler_output/ hidden_states/attentions fields (removes 5 test skips) - Add @capture_outputs to Granite4VisionTextModel.forward so output_hidden_states is collected via hooks and propagated through to the causal LM output (fixes test_assisted_decoding_matches_greedy_search) - get_image_features: populate hidden_states from vision tower when output_hidden_states=True (via kwarg or config); removes test skip - Remove stale test_training skip (ForConditionalGeneration computes loss; base model is skipped automatically by MODEL_MAPPING_NAMES check) - Delete DynamicCache init block from Granite4VisionTextModel.forward; GenerationMixin._prepare_cache_for_generation handles this - Import BaseModelOutputWithPooling, capture_outputs; drop DynamicCache import Co-Authored-By: Claude Sonnet 4.6 <[email protected]> Signed-off-by: artemspector <[email protected]>

…ader Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

The test actually passes — framework's recursive apply() handles QFormer submodule weights correctly via the nn.Linear branch of _init_weights. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

…RMSNorm branches These were missing, causing test_can_init_all_missing_weights to fail in CI when weights initialized from meta device didn't match __init__ values. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

github-actions · 2026-05-03T07:39:28Z

[For maintainers] Suggested jobs to run (before merge)

run-slow: auto, granite4_vision

Granite4VisionTextRMSNorm is defined in the generated file, not modular, so referencing it by name in modular_granite4_vision.py is an undefined name. Use attribute-based detection (has weight + variance_epsilon, not Linear) instead. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

artem-spector · 2026-05-03T08:15:00Z

When from_pretrained(None, state_dict={}) walks submodules, our _init_weights has no isinstance branches for QFormer's internal layers (attention, embeddings, etc.), so they stay uninitialized.

That is weird, usually our initializer walks down each sub-module and initializes its weights. Unless Qformer has special buffers that need to be init, it should be fine. I can check it on Monday and do another review pass

will re-check too

you right, test_can_init_all_missing_weights works

zucchini-nlp

Nice, last comments on style, flagged a few that weren't addressed/pushed from prev review

Approving so we will request core maintainer review to merge after fixing left-over comments

zucchini-nlp · 2026-05-04T13:28:58Z

        ("glm_image", {"pil": "GlmImageImageProcessorPil", "torchvision": "GlmImageImageProcessor"}),
        ("glpn", {"pil": "GLPNImageProcessorPil", "torchvision": "GLPNImageProcessor"}),
        ("got_ocr2", {"pil": "GotOcr2ImageProcessorPil", "torchvision": "GotOcr2ImageProcessor"}),
+        ("granite4_vision", {"pil": "LlavaNextImageProcessorPil", "torchvision": "LlavaNextImageProcessor"}),


this belong in image_processing_auto.py. Was it auto-generated or added manually?

zucchini-nlp · 2026-05-04T13:34:58Z

+        # Peek at vision hidden_size before super() to build a fully-specified qformer_config,
+        # avoiding any runtime field patching after super().
+        if isinstance(self.vision_config, dict):
+            vision_hidden_size = self.vision_config.get("hidden_size", 1152)
+        elif self.vision_config is not None:
+            vision_hidden_size = self.vision_config.hidden_size
+        else:
+            vision_hidden_size = 1152
+


vision/text configs cannot be None, no? In other words, we create a default config if not passes, just like with qformer below. Then we won't need a magical 1152 hardcoded here

If that is about the order, you can call super() at the beginning and it will unwrap first. Also we can completely override __post_init__ by calling PreTrainedConfig.__post_init__(**kwargs) instead of super

zucchini-nlp · 2026-05-04T13:38:32Z

+        return (
+            features.view(batch, side, side, channels)
+            .view(batch, num_win, window_size, num_win, window_size, channels)
+            .transpose(2, 3)
+            .flatten(0, 2)
+            .flatten(1, 2)
+        )


ultra nit: for sake of readability I prefer to split into several lines. Same comment for unwindowed_raster

zucchini-nlp · 2026-05-04T13:40:13Z

+        if isinstance(module, Granite4VisionTextRotaryEmbedding):
+            # Non-persistent buffers (inv_freq, original_inv_freq) are replaced with
+            # torch.empty_like() garbage by _move_missing_keys_from_meta_to_device.
+            # Recompute them here so _initialize_missing_keys restores correct values.
+            rope_type = module.config.rope_parameters.get("rope_type", "default")
+            if rope_type != "default":
+                rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type]
+            else:
+                rope_init_fn = module.compute_default_rope_parameters
+            inv_freq, attention_scaling = rope_init_fn(module.config, module.inv_freq.device)
+            init.copy_(module.inv_freq, inv_freq)
+            init.copy_(module.original_inv_freq, inv_freq)


didn't push commit? 😄 Same for LN and embed modules, calling super is usually enough unless model requires special weight init

zucchini-nlp · 2026-05-04T13:41:32Z

+    base_model_prefix = "model"
+    _no_split_modules = ["Granite4VisionTextDecoderLayer"]


same comment, I think you have one local commit unpushed

zucchini-nlp · 2026-05-04T13:46:37Z

+        vision_feature_layer = (
+            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+        )
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )


we can also delete these and decorate merge_defaults_with_config. Also you can delete the decorator in L669 and it will be copied from llava by modular

zucchini-nlp · 2026-05-04T13:47:18Z

+        deepstack_features = None
+        vision_mask = None
+        image_features = None
+        if pixel_values is not None and pixel_values.size(0) > 0:


bad copy from llava-next, checking size(0) shoulnd't be needed

zucchini-nlp · 2026-05-04T13:48:19Z

+                concat_features = torch.cat(packed_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+                if idx == 0:
+                    vision_mask = self.get_image_token_mask(
+                        input_ids, inputs_embeds=inputs_embeds, image_features=concat_features
+                    )
+                    inputs_embeds = inputs_embeds.masked_fill(vision_mask, 0.0)
+                deepstack_features.append((llm_layer_idx, concat_features))
+


i see, could you add a small inline comment that model relies only on deepstack_features and assigning 0 value is on purpose

zucchini-nlp · 2026-05-04T13:49:23Z

+        vision_feature_layer = (
+            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+        )
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )


same here re decorarots and x if x else self.x

zucchini-nlp · 2026-05-04T13:51:01Z

+        # Bypass nn.Module.__call__ overhead by calling the unbound forward directly.
+        # nn.Module.__call__ has non-trivial per-call overhead that accumulates across 40 layers × N steps.
+        outputs = Granite4VisionTextModel.forward(
+            self.language_model,
+            input_ids=None,


looks weird, is that smth from modular or simply calling self.language_model has an overhead? Do you have an intuition what could be reason, I don't think we can keep it this way

zucchini-nlp · 2026-05-04T13:53:34Z

run-slow: granite4_vision

artem-spector force-pushed the add-gv41 branch from 5ae88fd to 5160359 Compare April 23, 2026 07:59

artem-spector force-pushed the add-gv41 branch from 5160359 to da43584 Compare April 23, 2026 09:21

artem-spector force-pushed the add-gv41 branch from da43584 to ede8894 Compare April 23, 2026 11:13

zucchini-nlp reviewed Apr 27, 2026

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evalstate mentioned this pull request Apr 28, 2026

Cumulative defect fixes from recent Transformers PRs evalstate/transformers#41

Open

artem-spector force-pushed the add-gv41 branch from 2f2f523 to d827a20 Compare April 29, 2026 08:24

zucchini-nlp reviewed Apr 29, 2026

View reviewed changes

This was referenced Apr 29, 2026

Cumulative feature and defect updates from recent Transformers PRs evalstate/transformers#42

Open

Cumulative defect fixes from recent Transformers PRs evalstate/transformers#43

Open

artem-spector force-pushed the add-gv41 branch from d7ff9a5 to 3bf488f Compare April 30, 2026 15:25

artemspector and others added 11 commits May 3, 2026 10:10

Fix conflict marker in image_processing_auto.py

de314a8

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Fix check-repo: remove spatial_stride (unused in modeling), fix auto_…

29f88f9

…mappings duplicate Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Fix duplicate legacy key in conversion_mapping.py

d0093d9

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Regenerate auto_mappings.py after rebase onto upstream/main

84374b3

Co-Authored-By: Claude Sonnet 4.6 <[email protected]> Signed-off-by: artemspector <[email protected]>

Revert dependency_versions_table.py to match setup.py (upstream state)

61a8e87

Signed-off-by: artemspector <[email protected]>

artemspector and others added 21 commits May 3, 2026 10:10

Address remaining review items 20 and 29

103e842

- Item 20: drop get_image_token_mask override, use parent's get_placeholder_mask - Item 29: delete test_image_processing_granite4_vision.py (identical to LlavaNext) Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Fix missing imports in modular: math, AutoConfig, select_best_resolution

476e8a7

Needed for ruff F821 (undefined name) to pass under make style. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Remove autodoc entries for ImageProcessor classes that don't exist in…

d84a896

… this model Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Fix model card date for add_dates.py check

e9268a2

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Fix dependency_versions_table.py: sync mlinter version with setup.py

ee05b3e

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Regenerate modeling/processing from updated modular; fix copyright he…

71202cf

…ader Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Fix ruff formatting in modular_granite4_vision.py

98e29ea

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Fix add_dates.py: update granite4_vision model card date to 2026-04-30

0848d92

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Remove incorrect skip from test_can_init_all_missing_weights

ff2d78e

The test actually passes — framework's recursive apply() handles QFormer submodule weights correctly via the nn.Linear branch of _init_weights. Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

artem-spector force-pushed the add-gv41 branch from d08e446 to ff2d78e Compare May 3, 2026 07:10

artemspector and others added 2 commits May 3, 2026 10:16

Fix add_dates.py: update granite4_vision model card date to 2026-05-03

4dcdf3e

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

artemspector and others added 2 commits May 3, 2026 10:42

Fix ruff formatting in modular_granite4_vision.py

3224b69

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

zucchini-nlp approved these changes May 4, 2026

View reviewed changes

+              ```bibtex
+              @misc{granite-vision-4.1-4b,
+                title={Granite Vision 4.1},
+                author={IBM Granite Vision Team},
+                year={2026},
+                url={https://huggingface.co/ibm-granite/granite-vision-4.1-4b}
+              }
+              ```

+                  device=0,
+                  torch_dtype=torch.bfloat16,
+              )



		@dataclass
		class Granite4VisionImageFeaturesOutput(ModelOutput):

		base_model_prefix = "model"
		_no_split_modules = ["Granite4VisionTextDecoderLayer"]

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artem-spector commented Apr 23, 2026

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