Add Granite 4.1 Vision as built-in multimodal model by artem-spector · Pull Request #40282 · vllm-project/vllm

artem-spector · 2026-04-19T11:55:49Z

Summary

Add built-in support for Granite 4.1 Vision (granite4_vision), a multimodal model with deepstack vision-to-LLM feature injection and window Q-Former downsampling
Model uses SigLip2 vision encoder, injects features at multiple LLM layers via masked addition (deepstack), with optional spatial offset sampling
Includes _CONFIG_REGISTRY entry so vLLM can load the model without upstream HF transformers support
Model ships with a same-repo LoRA adapter; tests use default_mm_loras for automatic adapter activation

Files added/modified

File	Purpose
`vllm/model_executor/models/granite4_vision.py`	Model implementation (deepstack injection, multimodal interface)
`vllm/model_executor/models/registry.py`	Register in `_MULTIMODAL_MODELS`
`vllm/transformers_utils/configs/granite4_vision.py`	Config class for `_CONFIG_REGISTRY`
`vllm/transformers_utils/configs/__init__.py`	Config import
`vllm/transformers_utils/config.py`	`_CONFIG_REGISTRY` entry
`vllm/transformers_utils/processors/granite4_vision.py`	Processor class
`vllm/transformers_utils/processors/__init__.py`	Processor import
`docs/models/supported_models.md`	Add to supported models list
`tests/models/registry.py`	Registry test entry
`tests/models/multimodal/generation/test_common.py`	VLM correctness test (HF vs vLLM)

Test plan

pytest tests/models/test_registry.py -k "Granite4Vision" — model loading (1 passed)
pytest tests/models/multimodal/generation/test_common.py -k "granite4_vision" — HF vs vLLM output comparison with LoRA adapter (1 passed)
pytest tests/models/multimodal/processing/test_common.py -k "ibm-granite/granite-vision-4.1-4b" — multi-modal processing correctness (3 passed)

claude

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

github-actions · 2026-04-19T11:55:56Z

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mergify · 2026-04-19T11:56:33Z

Documentation preview: https://vllm--40282.org.readthedocs.build/en/40282/

gemini-code-assist

Code Review

This pull request adds support for the Granite 4 Vision model, implementing its architecture which utilizes a SigLIP vision encoder and WindowQFormer projectors for deepstack feature injection into a Granite language backbone. The changes include the model implementation, custom configuration, and a specialized processor. Feedback indicates that storing request-specific state on the model instance is not thread-safe and could lead to race conditions. Additionally, the use of tensor cloning in the layer loop was identified as a performance bottleneck, and the manual LoRA merging logic was flagged as fragile and potentially incompatible with quantization schemes.

gemini-code-assist · 2026-04-19T11:58:17Z

+        Uses _STACKED_PARAMS_MAPPING + module._get_shard_offset_mapping()
+        to handle packed QKV correctly (works with GQA automatically).
+        """
+        lora_alpha = adapter_config.get("lora_alpha", 1)
+        lora_r = adapter_config.get("r", 1)
+        scaling = lora_alpha / lora_r
+
+        # Collect lora_A / lora_B by vLLM module key
+        lora_a: dict[str, torch.Tensor] = {}
+        lora_b: dict[str, torch.Tensor] = {}
+        for peft_key, tensor in adapter_weights.items():
+            if ".lora_A." in peft_key:
+                module_key = self._peft_to_vllm(
+                    peft_key.replace(".lora_A.weight", ""))
+                lora_a[module_key] = tensor
+            elif ".lora_B." in peft_key:
+                module_key = self._peft_to_vllm(
+                    peft_key.replace(".lora_B.weight", ""))
+                lora_b[module_key] = tensor
+
+        params_dict = dict(self.named_parameters())
+        modules_dict = dict(self.named_modules())
+
+        def _add_delta(name: str, delta: torch.Tensor) -> bool:
+            # Try stacked/fused params first (qkv_proj, gate_up_proj)
+            for fused_name, orig_name, shard_id in self._STACKED_PARAMS_MAPPING:
+                if orig_name not in name:
+                    continue
+                fused_param_name = name.replace(orig_name, fused_name)
+                if fused_param_name not in params_dict:
+                    continue
+                param = params_dict[fused_param_name]
+                module_path = fused_param_name.rsplit(".weight", 1)[0]
+                module = modules_dict.get(module_path)
+                if module is None:
+                    continue
+
+                tp_rank = get_tensor_model_parallel_rank()
+                tp_size = get_tensor_model_parallel_world_size()
+
+                if hasattr(module, "_get_shard_offset_mapping"):
+                    # QKVParallelLinear: string shard_id ("q", "k", "v")
+                    shard_offset = module._get_shard_offset_mapping(shard_id)
+                    if shard_offset is not None:
+                        shard_size = delta.shape[0] // tp_size
+                        tp_delta = delta.narrow(
+                            0, tp_rank * shard_size, shard_size)
+                        shard = param.data[shard_offset:shard_offset + shard_size]
+                        param.data[shard_offset:shard_offset + shard_size] = (
+                            shard.float() + tp_delta.to(shard.device)
+                        ).to(shard.dtype)
+                        return True
+                elif hasattr(module, "output_sizes") and isinstance(shard_id, int):
+                    # MergedColumnParallelLinear: integer shard_id (0, 1)
+                    shard_size = module.output_sizes[shard_id] // tp_size
+                    shard_offset = sum(
+                        s // tp_size for s in module.output_sizes[:shard_id]
+                    )
+                    tp_delta = delta.narrow(
+                        0, tp_rank * (delta.shape[0] // tp_size),
+                        delta.shape[0] // tp_size)
+                    shard = param.data[shard_offset:shard_offset + shard_size]
+                    param.data[shard_offset:shard_offset + shard_size] = (
+                        shard.float() + tp_delta.to(shard.device)
+                    ).to(shard.dtype)
+                    return True
+            # Direct param (o_proj, down_proj)
+            if name in params_dict:
+                param = params_dict[name]
+                # Under TP, param is already sharded but delta is full-size.
+                # Slice delta to match: dim 0 for column-parallel, dim 1 for
+                # row-parallel.
+                if delta.shape != param.data.shape:
+                    tp_rank = get_tensor_model_parallel_rank()
+                    for dim in range(delta.dim()):
+                        if delta.shape[dim] != param.data.shape[dim]:
+                            shard_size = param.data.shape[dim]
+                            offset = tp_rank * shard_size
+                            delta = delta.narrow(dim, offset, shard_size)
+                            break
+                merged = param.data.float() + delta.to(param.device)
+                param.data = merged.to(param.dtype)
+                return True
+            return False
+
+        merge_device = next(self.parameters()).device
+        merged = 0
+        for module_key in sorted(lora_a):
+            if module_key not in lora_b:
+                logger.warning("LoRA B missing for %s, skipping", module_key)
+                continue
+            A = lora_a[module_key].to(merge_device).float()
+            B = lora_b[module_key].to(merge_device).float()
+            delta = scaling * (B @ A)
+            if _add_delta(module_key + ".weight", delta):
+                merged += 1
+            else:
+                logger.warning("LoRA target not found: %s", module_key)
+
+        return merged


The manual LoRA merging logic in _merge_lora_deltas is fragile and incompatible with quantization. It directly manipulates param.data assuming it is a standard floating-point tensor, which will fail or produce incorrect results for models using AWQ, GPTQ, FP8, or other quantization schemes where weights are packed or scaled. Additionally, this logic duplicates complex Tensor Parallelism (TP) sharding and fused-weight mapping (QKV, gate-up) that is already robustly handled by vLLM's core infrastructure. This custom implementation bypasses vLLM's native LoRA support and creates a significant maintenance burden. It is strongly recommended to remove this logic and rely on vLLM's existing LoRA features or provide pre-merged checkpoints.

Resolved in bb6a963 — dropped the entire merge-on-load flow. Native vLLM LoRA serving (--enable-lora --default-mm-loras) is the supported path for LoRA adapters.

DarkLight1337

Thanks, some initial comments

DarkLight1337 · 2026-04-20T05:29:45Z

@@ -425,6 +445,20 @@
        auto_cls=AutoModelForImageTextToText,
        vllm_output_post_proc=model_utils.llava_image_vllm_to_hf_output,
    ),
+    "granite4_vision": VLMTestInfo(


Please keep in alphabetical order

Done in a92e14d.

DarkLight1337 · 2026-04-20T05:30:50Z

+from safetensors.torch import load_file
+from transformers import BatchFeature
+from transformers.models.blip_2.configuration_blip_2 import Blip2QFormerConfig
+from transformers.models.blip_2.modeling_blip_2 import Blip2QFormerModel


You can use the Blip2QFormerModel from vLLM

Done in b3ac386 — switched WindowQFormerDownsampler to use vLLM's Blip2QFormerModel from blip2.py

DarkLight1337 · 2026-04-20T10:39:46Z

/gemini review

gemini-code-assist

Code Review

This pull request adds support for the Granite 4 Vision model, including its SigLIP vision encoder and deepstack feature injection architecture. The changes encompass model implementation, configuration, and processor integration. Review feedback highlighted critical issues: the deepstack injection is incompatible with Pipeline Parallelism, the embedding logic is memory-inefficient and potentially problematic for CUDA graphs, the manual LoRA merging lacks quantization awareness, and a logic error was found in the test post-processor.

gemini-code-assist · 2026-04-20T10:42:27Z

+    ) -> torch.Tensor | IntermediateTensors:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.embed_input_ids(input_ids)
+                hidden_states = hidden_states * self.config.embedding_multiplier
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+
+        for layer_idx, layer in islice(
+            enumerate(self.layers), self.start_layer, self.end_layer
+        ):
+            if deepstack_input_embeds is not None:
+                key = f"ds_{layer_idx}"
+                if key in deepstack_input_embeds.tensors:
+                    feat = deepstack_input_embeds[key]
+                    # Resize to match hidden_states in case of CUDA graph padding
+                    num_tokens = hidden_states.size(0)
+                    buf_len = feat.shape[0]
+                    if buf_len != num_tokens:
+                        feat = torch.nn.functional.pad(
+                            feat[:num_tokens],
+                            (0, 0, 0, max(0, num_tokens - buf_len)),
+                        )
+                    hidden_states = hidden_states + feat
+            hidden_states = layer(positions, hidden_states)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({"hidden_states": hidden_states})
+
+        hidden_states = self.norm(hidden_states)
+        return hidden_states


The current implementation of Pipeline Parallelism (PP) for Deepstack injection is broken. The deepstack_input_embeds are only created on the first PP rank and are not passed to subsequent ranks. Consequently, layers on ranks other than the first will not receive the required feature injections. To fix this, the deepstack tensors should be included in the IntermediateTensors passed between PP stages, and each rank should extract the tensors corresponding to its assigned layers.

Fixed in commit 453479a.

The fix has three parts:

make_empty_intermediate_tensors on the right class — overridden on Granite4VisionLLMForCausalLM (not on Granite4VisionLLMModel), which is the class vLLM actually calls. It adds a ds_{layer} key for each deepstack target layer so the
PP receive buffer is pre-allocated with the right shape.

_ds_layer_indices accessible to the override — the inner LLM model only sees text_config (no deepstack_layer_map), so the outer model sets self.language_model._ds_layer_indices after construction.

Full-size buffers sent between ranks — sync_and_slice_intermediate_tensors copies copy_len = num_tokens_padded rows (CUDA graph bucket size), which can exceed actual token count. Sending the full pre-allocated buffer (max_tokens × hidden_size) ensures the copy always succeeds.

gemini-code-assist · 2026-04-20T10:42:27Z

+            buf_data = torch.zeros(N, lm_h, dtype=inputs_embeds.dtype, device=inputs_embeds.device)
+            buf_data[is_multimodal] = level_features[level_idx]
+            self._ds_buffers[level_idx][:N].copy_(buf_data)


This loop creates a new large zero tensor (buf_data) on every call to embed_input_ids for each deepstack level. This is highly inefficient and can lead to memory pressure or OOM when N (batch size) is large. Since self._ds_buffers are already pre-allocated persistent buffers, you should scatter the multimodal features directly into a slice of the persistent buffer after zeroing it. Additionally, initializing these buffers on CPU (line 549) and moving them to GPU on the first request can cause a crash during CUDA graph capture if the first requests are text-only, as the graph will capture operations on CPU tensors. It is recommended to register these as buffers using self.register_buffer in __init__ to ensure they are correctly moved to the GPU with the model.

Suggested change

buf_data = torch.zeros(N, lm_h, dtype=inputs_embeds.dtype, device=inputs_embeds.device)

buf_data[is_multimodal] = level_features[level_idx]

self._ds_buffers[level_idx][:N].copy_(buf_data)

for level_idx in range(len(self._ds_layer_indices)):

target_buf = self._ds_buffers[level_idx][:N]

target_buf.zero_()

target_buf[is_multimodal] = level_features[level_idx]

Fixed in 53c0b8c. Eliminated the per-call torch.zeros allocation — now zeroing the persistent buffer slice directly and scattering features into it:

target = self._ds_buffers[level_idx][:N] target.zero_() target[is_multimodal] = level_features[level_idx]

On the register_buffer suggestion: since _ds_buffers is a list of tensors rather than named attributes, register_buffer doesn't apply directly. The lazy device migration on first use is safe because text-only requests never reach embed_input_ids (they skip embed_multimodal entirely), so CUDA graph capture won't encounter CPU tensors.

gemini-code-assist · 2026-04-20T10:42:27Z

+                            0, tp_rank * shard_size, shard_size)
+                        shard = param.data[shard_offset:shard_offset + shard_size]
+                        param.data[shard_offset:shard_offset + shard_size] = (
+                            shard.float() + tp_delta.to(shard.device)


The manual LoRA merging logic is not quantization-aware. It attempts to cast shard.data to float, add a delta, and cast back to the original dtype. If the model is quantized (e.g., AWQ or FP8), shard.dtype will be a quantized type (like torch.int32 for packed AWQ weights), and this operation will produce incorrect results or fail. This 'Full-merge' feature should either check if the model is quantized and skip merging with a warning, or use a quantization-aware merging mechanism. Users should be encouraged to use vLLM's native LoRA support (--enable-lora) which handles quantized models correctly.

Resolved in bb6a963 — dropped the entire merge-on-load flow. Native vLLM LoRA serving (--enable-lora --default-mm-loras) is the supported path for LoRA adapters.

gemini-code-assist · 2026-04-20T10:42:27Z

+    hf_output_ids = [
+        token_id
+        for idx, token_id in enumerate(output_ids)
+        if token_id != mm_token_id or output_ids[idx - 1] != mm_token_id


There is a logic error in the list comprehension when idx == 0. The condition output_ids[idx - 1] will check the last token of the sequence (output_ids[-1]) when processing the first token. If the sequence starts and ends with mm_token_id, the first token will be incorrectly skipped. The condition should explicitly handle the first index to ensure it is always kept if it matches the criteria.

Suggested change

if token_id != mm_token_id or output_ids[idx - 1] != mm_token_id

if token_id != mm_token_id or idx == 0 or output_ids[idx - 1] != mm_token_id

Fixed in 53c0b8c. Added the explicit idx == 0 guard:
if token_id != mm_token_id or idx == 0 or output_ids[idx - 1] != mm_token_id

Adds granite4_vision (Granite4VisionForConditionalGeneration) with GraniteForCausalLM backbone, SigLIP vision encoder, and deepstack feature injection via WindowQFormer projectors. Includes config/processor for _CONFIG_REGISTRY bypass, model registry, docs, and test entry. Signed-off-by: Artem Spector <artems@il.ibm.com> Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

init_vision_tower_for_llava truncates the encoder to vision_feature_layer depth, but deepstack needs ALL hidden states (deepstack_layer_map uses indices into the full encoder output list). Use SiglipVisionModel directly and update the weight mapping prefix accordingly. Also removes debug dump instrumentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

Add VLMTestInfo entry for Granite 4.1 Vision in test_common.py: - Single image correctness test (HF vs vLLM output comparison) - LoRA adapter support via default_mm_loras (same-repo adapter) - Self-contained post-processor to avoid trust_remote_code issues with AutoConfig/AutoTokenizer for models not yet in upstream HF Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

Replace hidden_states.clone() + indexed assignment with in-place +=. No autograd in vLLM inference, so the defensive copy is unnecessary. Eliminates up to 8 full tensor clones per forward pass. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

- Move granite4_vision test entry to alphabetical position - Replace getattr(config, ...) with direct config attribute access Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

The base model repo uses different field names (vision_layer_to_llm_layer, checkerboard_*) than our config class (deepstack_layer_map, spatial_*). Accept both naming conventions so the model loads from either source. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

Replaces the transformers import with vLLM's built-in Blip2QFormerModel from blip2.py. Passes quant_config, cache_config, and prefix through WindowQFormerDownsampler to the QFormer, matching the pattern used by GraniteSpeech. Removes return_dict=True (vLLM returns raw tensor). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

Three bugs prevented vision from working with enforce_eager=False: 1. _ds_layer_indices was populated lazily (first embed_multimodal call), so it was empty during CUDA graph capture. Forward passed ds=None, capturing the graph without any injection code path. Fix: pre-populate _ds_layer_indices from config in __init__. 2. forward() only passed deepstack when _ds_num_tokens > 0, so CUDA graph capture (which has no real images) captured without injection. Fix: always pass deepstack buffers (zero-filled = no-op) when inputs_embeds is non-None, so the graph captures the injection path. 3. pbuf[:N][is_multimodal] = feat is a PyTorch no-op — boolean indexing on a slice returns a copy, not a view. Buffers stayed all zeros. Fix: build a full (N, lm_h) buffer tensor first, then copy_ into the persistent pre-allocated buffer (matches Qwen3-VL pattern). Also fixes stale buffer leak: after each prefill, zero the buffers and reset _ds_num_tokens so the next request (text-only or new vision) does not inherit features from the previous request. Also adds Granite4VisionLLMModel / Granite4VisionLLMForCausalLM classes (DarkLight1337's request) so the deepstack layer loop lives in a proper LLM subclass rather than the outer model's forward(). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

- Fix weight mapper: model.vision_tower. -> vision_tower. (the new checkpoint already includes vision_model. in the key path, so the old mapping was producing a double vision_model.vision_model. prefix) - Update test_common.py and registry.py to use the official model ID Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

mergify · 2026-04-20T13:18:34Z

Hi @artem-spector, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

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With PP=2, rank 0 runs layers 0–19 and rank 1 runs layers 20–39. Deepstack feature buffers for rank 1's target layers (e.g. ds_21) must be included in the IntermediateTensors handed off between ranks. Three co-dependent fixes: 1. Override make_empty_intermediate_tensors on Granite4VisionLLMForCausalLM (not on Granite4VisionLLMModel): GraniteForCausalLM.make_empty_intermediate_tensors does not delegate to self.model, so the override must live on the causal wrapper to be reachable by vLLM's PP machinery. 2. Set self.language_model._ds_layer_indices from the outer model after construction so make_empty_intermediate_tensors can enumerate the ds keys (text_config alone has no deepstack_layer_map). 3. Send full-size buffers (shape max_tokens × H) rather than a sliced view [:n] when forwarding ds tensors to PP rank 1. The framework's sync_and_slice_intermediate_tensors copies with copy_len = padded token count (CUDA graph bucket size), which may exceed the actual token count n; sliced tensors caused a RuntimeError on the shape mismatch. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

mergify · 2026-04-20T13:25:50Z

Hi @artem-spector, the pre-commit checks have failed. Please run:

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Fix logic error in granite4_vision post-processor: when idx==0, output_ids[idx-1] wraps to the last element, incorrectly skipping the first token if it matches mm_token_id. Add an explicit idx==0 check to always keep the first token. In embed_input_ids, eliminate the per-call torch.zeros allocation inside the deepstack level loop. Instead zero the persistent buffer slice directly and scatter features into it, removing the intermediate allocation and copy. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

mergify · 2026-04-20T13:43:59Z

Hi @artem-spector, the pre-commit checks have failed. Please run:

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mergify · 2026-04-20T14:05:00Z

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

DarkLight1337

Can you add this model to the offline inference vision examples? e.g. https://github.com/vllm-project/vllm/blob/main/examples/offline_inference/vision_language_multi_image.py

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

DarkLight1337

Thanks for your patience!

Remove _merge_lora_deltas, _apply_adapter, _load_adapter, _peft_to_vllm, and _STACKED_PARAMS_MAPPING. Native vLLM LoRA serving (--enable-lora --default-mm-loras) is the supported path; the manual merge-on-load path was not quantization-aware and fragile under TP. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

artem-spector · 2026-04-21T10:03:29Z

CI tests fail because the model ibm-granite/granite-vision-4.1-4b is not public yet.
We could give an access for your CI user to the model, would that be acceptable?

DarkLight1337 · 2026-04-21T10:28:00Z

Let's just set is_available_online=False until it's released

Model ibm-granite/granite-vision-4.1-4b is not yet public; setting is_available_online=False prevents CI from attempting to download it. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

Signed-off-by: Artem Spector <artems@il.ibm.com> Signed-off-by: artemspector <artems@il.ibm.com> Co-authored-by: artemspector <artems@il.ibm.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: hongbolv <33214277+hongbolv@users.noreply.github.com>

Signed-off-by: Artem Spector <artems@il.ibm.com> Signed-off-by: artemspector <artems@il.ibm.com> Co-authored-by: artemspector <artems@il.ibm.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

Signed-off-by: Artem Spector <artems@il.ibm.com> Signed-off-by: artemspector <artems@il.ibm.com> Co-authored-by: artemspector <artems@il.ibm.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: Yifan <yzong@redhat.com>

Signed-off-by: Artem Spector <artems@il.ibm.com> Signed-off-by: artemspector <artems@il.ibm.com> Co-authored-by: artemspector <artems@il.ibm.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: Avinash Singh <avinashsingh.rcoem@gmail.com>

Signed-off-by: Artem Spector <artems@il.ibm.com> Signed-off-by: artemspector <artems@il.ibm.com> Co-authored-by: artemspector <artems@il.ibm.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: Adrian <info@zzit.ch>

Signed-off-by: Artem Spector <artems@il.ibm.com> Signed-off-by: artemspector <artems@il.ibm.com> Co-authored-by: artemspector <artems@il.ibm.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: hongbolv <33214277+hongbolv@users.noreply.github.com>

Signed-off-by: Artem Spector <artems@il.ibm.com> Signed-off-by: artemspector <artems@il.ibm.com> Co-authored-by: artemspector <artems@il.ibm.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

Signed-off-by: Artem Spector <artems@il.ibm.com> Signed-off-by: artemspector <artems@il.ibm.com> Co-authored-by: artemspector <artems@il.ibm.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Signed-off-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>

artem-spector requested review from DarkLight1337 and ywang96 as code owners April 19, 2026 11:55

claude Bot reviewed Apr 19, 2026

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mergify Bot added documentation Improvements or additions to documentation multi-modality Related to multi-modality (#4194) new-model Requests to new models labels Apr 19, 2026

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artem-spector force-pushed the add-gv41 branch from da5dd98 to f80b0df Compare April 19, 2026 12:16

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artem-spector force-pushed the add-gv41 branch from b3ac386 to e4aa0fb Compare April 20, 2026 07:08

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artemspector and others added 8 commits April 20, 2026 13:43

artem-spector force-pushed the add-gv41 branch from 35bb790 to 77b778e Compare April 20, 2026 10:43

DarkLight1337 added the verified Run pre-commit for new contributors without triggering other tests label Apr 20, 2026

Apply ruff formatting to configs and processors files

bb288ae

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

DarkLight1337 reviewed Apr 20, 2026

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Add granite4_vision to offline inference multi-image example

819e4ea

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Signed-off-by: artemspector <artems@il.ibm.com>

DarkLight1337 approved these changes Apr 21, 2026

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DarkLight1337 enabled auto-merge (squash) April 21, 2026 03:23

github-actions Bot added the ready ONLY add when PR is ready to merge/full CI is needed label Apr 21, 2026

auto-merge was automatically disabled April 21, 2026 09:39
Head branch was pushed to by a user without write access

DarkLight1337 enabled auto-merge (squash) April 21, 2026 10:38

DarkLight1337 merged commit d249a9e into vllm-project:main Apr 21, 2026
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artem-spector mentioned this pull request Apr 23, 2026

Add Granite 4.1 Vision (granite4_vision) huggingface/transformers#45597

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artem-spector mentioned this pull request Apr 27, 2026

[Bugfix][Granite4Vision] Fix deepstack buffer causing decode slowdown in compiled mode #40917

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tarekziade mentioned this pull request May 7, 2026

Add Granite 4.1 Vision (granite4_vision) tarekziade/tarekziade-transformers-reviewer-test#16

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4 tasks

-            buf_data = torch.zeros(N, lm_h, dtype=inputs_embeds.dtype, device=inputs_embeds.device)
-            buf_data[is_multimodal] = level_features[level_idx]
-            self._ds_buffers[level_idx][:N].copy_(buf_data)
+        for level_idx in range(len(self._ds_layer_indices)):
+            target_buf = self._ds_buffers[level_idx][:N]
+            target_buf.zero_()
+            target_buf[is_multimodal] = level_features[level_idx]

	if token_id != mm_token_id or output_ids[idx - 1] != mm_token_id
	if token_id != mm_token_id or idx == 0 or output_ids[idx - 1] != mm_token_id

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artem-spector commented Apr 19, 2026 •

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