[Core][Model] PrithviMAE Enablement on vLLM v1 engine #20072

christian-pinto · 2025-06-25T11:35:21Z

Purpose

This PR enables the Prithvi MAE Geospatial model on the vLLM V1 engine. This is a result of pooling/embedding models being enabled in V1.

The main changes in this PR are:

Adding support for multi-modal models that require the raw multi-modal data in input and not the multi_modal embeddings.
Improving support for models that use skip_tokenizer_init=True
Improving support for attention-less models by creating a DummyKVCacheManager class
Minor changes to the PrithviMAE model and the example usage script.

Test Plan

I have added a test script that is only testing the with vLLM runner as this model is not on HF. Therefore it would only check if the inference takes place correctly for the model.

Test Result

The test is not succeeding at the moment because the pytest process hangs indefinitely while initializing the vlLM engine. The test hangs indefinitely on wait_for_engine_startup. The same code tested outside of pytest works just fine. Any help on getting this test running would be much appreciated.

Documentation Update

Updated examples file.

@DarkLight1337 @maxdebayser @njhill

gemini-code-assist

Summary of Changes

Hello @christian-pinto, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly extends vLLM's capabilities by enabling the Prithvi MAE Geospatial model on the V1 engine. It introduces foundational changes to support multi-modal models that require raw input data, streamlines the handling of attention-less models by providing a dummy KV cache mechanism, and refines the engine's behavior for models that do not require tokenizer initialization.

Highlights

Multi-modal Support: Added support for multi-modal models that consume raw input data (e.g., image tensors) directly, rather than pre-computed embeddings. This includes new interfaces (SupportsMultiModalWithRawInput) and integration points in the model runner to pass raw data to the model's forward pass.
Attention-Free Models: Introduced a DummyKVCacheManager and updated the scheduler and engine initialization to properly handle models that do not require a KV cache (attention-free models). This prevents unnecessary resource allocation and potential hangs by bypassing KV cache operations for such models.
Tokenizer Initialization: Enhanced support for models that specify skip_tokenizer_init=True, ensuring that tokenizer-related operations are conditionally bypassed in various parts of the engine (e.g., LLMEngine, Processor, OutputProcessor, StructuredOutput), improving flexibility for models that don't rely on a traditional tokenizer.
PrithviMAE Integration: Integrated the Prithvi MAE Geospatial model into the vLLM V1 engine, including updates to its model definition, multi-modal data processing (e.g., using MultiModalFieldConfig.shared and adjusting dummy data types), and an example inference script.

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Code Review

This pull request enables the Prithvi MAE Geospatial model on the vLLM V1 engine by adding support for multi-modal models that require raw multi-modal data, improving support for models that use skip_tokenizer_init=True, and enhancing support for attention-less models. The changes include modifications to the PrithviMAE model, example usage, configuration, and core components of vLLM. The changes look good, but I've added some suggestions for improved comments.

examples/offline_inference/prithvi_geospatial_mae.py

github-actions · 2025-06-25T11:38:14Z

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DarkLight1337 · 2025-06-25T14:30:45Z

vllm/config.py

+        self.is_pooling_model = self.registry.is_pooling_model(
+            self.architectures)


Make this a @property?

sure, no problem

DarkLight1337 · 2025-06-25T14:32:14Z

vllm/v1/core/kv_cache_manager.py

@@ -64,7 +65,130 @@ def new_empty(self) -> "KVCacheBlocks":
        return KVCacheBlocks(tuple([] for _ in range(len(self.blocks))))


-class KVCacheManager:
+class KVCacheManagerInterface(ABC):


Not sure whether this conforms with V1 design. cc @maxdebayser @heheda12345 @WoosukKwon

I have used the same interface template used for the Scheduler in v1. If another way of defining an interface is the preferred one, please let me know.

@DarkLight1337 , by V1 design do you mean "the right way of defining interfaces" or whether the KVCacheManager should be pluggable or not?

BTW, is there anyone working on pure SSM model support in V1?

@DarkLight1337 , by V1 design do you mean "the right way of defining interfaces" or whether the KVCacheManager should be pluggable or not?

Yeah, I'm not sure which parts of KVCacheManager should be customizable.

BTW, is there anyone working on pure SSM model support in V1?

#19327

I have investigated option 1 and it would require a number of small changes in the KVCacheManager, KVCacheCoordinator, etc. The logic is relying on the fact that there are > 0 kv_cache_group. I am not sure we end-up with a cleaner implementation. By overloading instead we just get rid of all the logic that would otherwise be useless when a KVCache is not used.

These are my two cents but I am happy to go for the solution that is more in line with the strategy for the KVCacheManager.

@heheda12345 any further comments on this? Thanks.

Want minor changes do you need? I feel it would be quite clean to treat it as 0 groups but maybe I've missed something.

What I have tried to do is to initialize the kv cache manager with the below config

KVCacheConfig(num_blocks=0, kv_cache_tensors=[], kv_cache_groups=[]) ]

This however it is not enough for the kvcache manager to function with 0 blocks.

I ended up having to modify also:

BlockPool in v1.core.block_pool.py because it uses a double linked (FreeKVCacheBlockQueue) list to track the free blocks. Such list is not designed to work with an empty list of kvcache blocks.

Scheduler because on each new request it invokes the allocate_blocks from the kvcache manager and if no blocks are returned requests are unschedulable. Some changes are then requires to make sure that requests with no associated block ids are schedulable.

I also had to do changes around to make sure the logic of the KV cache would work with no KV cache groups.

For simplicity I have tried implementing the other approach in this PR: #20577

I still remain of the idea that initializing all the objects that come with the KVCacheManager (KVCacheCoordinator, BLockPool, FreeKVCachePool, etc.) for no reason is less clean than overloading the KVCacheManager.

I might also have misunderstood your suggestions @heheda12345 on using 0 kv cache groups so please please take a look at his new PR.

Thanks

Thanks. I've added some comments in #20577 and feel that 0 kv cache group is possible.

maxdebayser

This PR introduces some changes that could be useful for future changes such as the implementation of pure SSM models and encoder embedding models. Currently the design of multi-modal inputs as embeddings seems pretty restrictive to me, but I don't know enough about all the design considerations that went into it. So to my superficial level of knowledge about the multi modal models, the support for raw inputs seems pretty useful.

maxdebayser · 2025-06-26T21:54:18Z

vllm/v1/engine/core.py

-        # `kv_cache_config` to be consistent across all workers to make sure
-        # all the memory operators can be applied to all workers.
-        unify_kv_cache_configs(kv_cache_configs)
+        if vllm_config.model_config.is_attention_free:


Could we perhaps generalize the name of this property to indicate that it's kv-cache free? Technically the encoder embedding models don't need a kv-cache but they do use attention.

I'd personally create a second flag that is a subset of this one to just disable KV Caching. In the end it is fine that for attention free models neither an attention backend nor a KVCache are instantiated.
At the moment I see that the initialization of the attention backend is done as part of initializing the KV Cache.

I believe this should be done in another PR, though. the changes here were simply because the is_attention_free flag was not fully working when I tested it.

maxdebayser · 2025-06-26T21:59:32Z

vllm/v1/worker/gpu_model_runner.py

@@ -547,6 +554,55 @@ def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
        if batch_changed or batch_reordered:
            self.input_batch.refresh_sampling_metadata()

+    def _add_multimodal_inputs_to_model_args(


If this function is only called in _maybe_add_multimodal_kwargs perhaps it's better to merge them.

yes, makes sense. Thanks

maxdebayser · 2025-06-26T22:01:02Z

vllm/v1/worker/gpu_model_runner.py

+                                                      scheduler_output,
+                                                      num_reqs)
+
+    def _maybe_compute_attn_prefix(


What is the purpose of this function? It looks like an extension point for a subclass but it's not called anywhere.

I think this is just something I have brought in when I started working on this based on support for embeddings on v1 before it got merged. I will remove it.

maxdebayser · 2025-06-26T22:01:11Z

vllm/v1/worker/gpu_model_runner.py

+    ) -> list[int]:
+        return [0] * len(self.kv_cache_config.kv_cache_groups)
+
+    def _maybe_prepare_additional_inputs(self,


Same as above.

Same as above 😄

aarnphm · 2025-06-27T01:28:41Z

vllm/v1/structured_output/__init__.py

-            scheduler_config=self.vllm_config.scheduler_config,
-            lora_config=self.vllm_config.lora_config,
-        ).get_lora_tokenizer(None)
+        self.tokenizer = None if vllm_config.model_config.skip_tokenizer_init \


How is this related to this PR?

btw I we can't skip tokenizer init here given that tokenizer is required for all of the structured outputs backend. therefore, this shouldn't be changed.

I see, this was one quick and dirty change that I made and forgot to sanitize.

Since the tokenizer is required for structured output generation the output manager should not be initialized at all when we skip tokenizer init. Also, all the call to the structured output manager should be guarded against it.

Let me give this a go and you can have a look at it.

@aarnphm I have reworked supporting skip_tokenizer_init so that it takes care of the structured output manager. Please have a look at commit 30ed44e

aarnphm

qq: does this work with transformers implementation?

aarnphm · 2025-06-27T15:55:30Z

qq: does this work with transformers implementation?

Ah i wad referring to --model-impl transformers but it doesn't seem to work. All good though

aarnphm

For models that doesn't support structured outputs, I think we should perform the gate at before server startup time.

For structured outputs overhead, I don't think skip_tokenizer_init is relevant here given that the manager initialize the tokenizer itself.

vllm/v1/engine/core.py

vllm/v1/core/sched/scheduler.py

mergify · 2025-07-02T01:12:25Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @christian-pinto.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

maxdebayser · 2025-07-02T12:49:51Z

vllm/model_executor/models/prithvi_geospatial_mae.py

+        # the input prompt we pass one token and the size of the dummy
+        #  embedding tensors must reflect that.
+        return torch.empty(input_ids.shape)
+


I don't see the get_multimodal_embeddings() function here. How is this not failing in GPUModelRunner.profile_run()?

I believe that memory profiling is only done while initializing the KVCache, which is skipped because the model is attention free.

It should happen in Worker.determine_available_memory() if I am not mistaken.

Yes, but if we're adding a new programming model for MM inputs I think it would be nice to handle this. If not a full solution, perhaps a test to disable cuda graphs and memory profiling for this special case.

But I'm thinking that maybe the raw MM inputs will be hard to generalize. Perhaps a hacky solution for this particular model would be to store the raw MM inputs in the get_multimodal_embeddings() method and then use them when forward() is called.

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

- Improved formatting around - made is_pooling_model a @Property in ModelConfig Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

- Remove unused functions - merged functions not called anywhere else Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

christian-pinto · 2025-07-04T09:40:17Z

Rebased on the latest master that includes @aarnphm changes to the structured output manager.

aarnphm

Ok, I did another round of review for other code misc to reduce general diff. I don't have an opinion for the KVCache part, so will leave that to others to comment on it.

aarnphm · 2025-07-04T15:09:23Z

vllm/v1/engine/output_processor.py

+                                                  parent_req=parent_req,
+                                                  request_index=request_index,
+                                                  queue=queue,
+                                                  log_stats=self.log_stats)


Suggested change

log_stats=self.log_stats)

log_stats=self.log_stats,)

to reduce diff

Done, thanks!

aarnphm · 2025-07-04T15:10:39Z

vllm/v1/engine/processor.py

+        if tokenizer:
+            max_input_id = max(prompt_ids, default=0)
+            if max_input_id > tokenizer.max_token_id:
+                raise ValueError(
+                    f"Token id {max_input_id} is out of vocabulary")


nit

Suggested change

if tokenizer:

max_input_id = max(prompt_ids, default=0)

if max_input_id > tokenizer.max_token_id:

raise ValueError(

f"Token id {max_input_id} is out of vocabulary")

if tokenizer and (max_input_id:=max(prompt_ids, default=0)) > tokenizer.max_token_id:

raise ValueError(f"Token id {max_input_id} is out of vocabulary")

aarnphm · 2025-07-04T15:12:12Z

vllm/v1/core/sched/scheduler.py

+                use_eagle=self.use_eagle,
+                log_stats=self.log_stats,
+                enable_kv_cache_events=self.enable_kv_cache_events,
+            )


Please revert back self.use_pp = ...

Nice catch, thank you.

aarnphm · 2025-07-04T15:12:30Z

vllm/v1/core/sched/scheduler.py

+                req_to_new_block_ids[request.request_id] = \
+                    self.kv_cache_manager.get_block_ids(request.request_id)


revert this style change. We will run ruff format at some point before 0.10

aarnphm · 2025-07-04T15:12:55Z

vllm/v1/core/sched/scheduler.py

+        # num_gpu_blocks can be ero for attention free models
+        assert num_gpu_blocks is not None


Suggested change

# num_gpu_blocks can be ero for attention free models

assert num_gpu_blocks is not None

# num_gpu_blocks can be zero for attention free models

assert num_gpu_blocks is not None

aarnphm · 2025-07-04T15:15:09Z

vllm/v1/engine/processor.py

+        if model_config.skip_tokenizer_init:
+            tokenizer = None
+        else:
+            tokenizer = self.tokenizer.get_lora_tokenizer(lora_request)


how does this look for diff

Suggested change

if model_config.skip_tokenizer_init:

tokenizer = None

else:

tokenizer = self.tokenizer.get_lora_tokenizer(lora_request)

tokenizer = None if model_config.skip_tokenizer_init \

else self.tokenizer.get_lora_tokenizer(lora_request)

aarnphm · 2025-07-04T15:15:50Z

vllm/v1/worker/gpu_model_runner.py

+
+        # nothing to be reordered when the mdoel is attention free
+        if self.model_config.is_attention_free:
+            return False


Suggested change

return False

return

aarnphm · 2025-07-04T15:16:14Z

vllm/v1/worker/gpu_model_runner.py

+    def _maybe_add_multimodal_kwargs(
+            self,
+            model_kwargs: dict[str, Any],
+            scheduler_output: "SchedulerOutput" = None,


Suggested change

scheduler_output: "SchedulerOutput" = None,

scheduler_output: "Optional[SchedulerOutput]" = None,

aarnphm · 2025-07-04T15:17:00Z

vllm/v1/worker/gpu_model_runner.py

+                                intermediate_tensors=intermediate_tensors,
+                                inputs_embeds=inputs_embeds,
+                                **MultiModalKwargs.as_kwargs(
+                                    model_kwargs, device=self.device))


Suggested change

model_kwargs, device=self.device))

model_kwargs, device=self.device),)

To also reduce the diff

heheda12345 · 2025-07-05T07:52:12Z

vllm/v1/core/kv_cache_manager.py

+
+    def get_computed_blocks(self,
+                            request: Request) -> tuple[KVCacheBlocks, int]:
+        return (KVCacheBlocks([]), 0)


What's your expect behavior of prefix caching? Seems that someone want prefix caching to be enabled in "last" pooling method.

vllm/vllm/config.py

Line 4594 in 8aeaa91

if pooling_type is None or pooling_type.lower() != "last":

heheda12345

Is the KVCacheManager modification necessary to this PR? As #16188 is runnable, is it possible to only include other necessary changes in this PR and split the KVCacheManager changes to another PR? It can both unblock this PR and help me to review KVCacheManager changes easier.

heheda12345 · 2025-07-08T04:19:00Z

vllm/v1/core/kv_cache_manager.py

@@ -64,7 +65,130 @@ def new_empty(self) -> "KVCacheBlocks":
        return KVCacheBlocks(tuple([] for _ in range(len(self.blocks))))


-class KVCacheManager:
+class KVCacheManagerInterface(ABC):


Thanks. I've added some comments in #20577 and feel that 0 kv cache group is possible.

christian-pinto · 2025-07-08T15:01:49Z

Is the KVCacheManager modification necessary to this PR? As #16188 is runnable, is it possible to only include other necessary changes in this PR and split the KVCacheManager changes to another PR? It can both unblock this PR and help me to review KVCacheManager changes easier.

Hi @heheda12345, unfortunately these changes are necessary or otherwise I will have to hack around a number of things including the profiling run for memory profiling as this is not a language model so profiling as it is does not work. These changes would be totally useless because I do not need any of this profiling to take place as this model does not use attention. Therefore I would endup making some changes that would be there solely to support my model until attention free models are supported and then revert everything back as it was.
Does this make sense?

To help you in reviewing the two options, in the new PR (the one using zero blocks) the changes for supporting attention free models are only in the last commit. Similarly in this PR all changes related to supporting attention free are in commits 953f66a and 953f66a

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

mergify · 2025-07-11T03:26:34Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @christian-pinto.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

christian-pinto requested review from DarkLight1337, ywang96, mgoin, russellb, aarnphm, WoosukKwon, robertgshaw2-redhat, njhill, comaniac and alexm-redhat as code owners June 25, 2025 11:35

christian-pinto changed the title ~~Prithvi v1 embeddings upstream main~~ PrithviMAE Enablement on vLLM v1 engine Jun 25, 2025

mergify bot added documentation Improvements or additions to documentation multi-modality Related to multi-modality (#4194) structured-output v1 labels Jun 25, 2025

github-project-automation bot added this to Structured Output Jun 25, 2025

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mgazz mentioned this pull request Jun 30, 2025

[RFC]: Support Prithvi geospatial model in serving mode #20234

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[Structured Outputs][V1] Skipping with models doesn't contain tokenizers #20365

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christian-pinto added 11 commits July 4, 2025 07:46

Support for attention free models in V1

953f66a

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Better support for skip_tokenizer_init=True

f174bbf

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Support for attention free models in V1

f3ab1fb

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Last few changes after rebasing to latest branch version

ff86cb0

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Support passing raw multimodal data to model

769d8dd

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

latest changes to align with the original branch

9cc76b1

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Latest changes to aadpt to upstream master

f8226da

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Some reformatting to make the pre-commit hooks succeed

c708266

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Few more changes to solve some other pre-commit hooks failures

0dba4cd

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Some style changes

e0ae311

- Improved formatting around - made is_pooling_model a @Property in ModelConfig Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

Simple code refactoring

aab8956

- Remove unused functions - merged functions not called anywhere else Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

christian-pinto force-pushed the prithvi_v1_embeddings_upstream_main branch from 30ed44e to aab8956 Compare July 4, 2025 09:38

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[Core][Model] PrithviMAE Enablement on vLLM v1 engine (with zero kv_cache_groups) #20577

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Some format chawnges to improve readability and diff sizes

3b7729e

Signed-off-by: Christian Pinto <christian.pinto@ibm.com>

mergify bot added the new-model Requests to new models label Jul 10, 2025

mergify bot added the needs-rebase label Jul 11, 2025

		self.is_pooling_model = self.registry.is_pooling_model(
		self.architectures)

		req_to_new_block_ids[request.request_id] = \
		self.kv_cache_manager.get_block_ids(request.request_id)

		# num_gpu_blocks can be ero for attention free models
		assert num_gpu_blocks is not None

	scheduler_output: "SchedulerOutput" = None,
	scheduler_output: "Optional[SchedulerOutput]" = None,

	model_kwargs, device=self.device))
	model_kwargs, device=self.device),)

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[Core][Model] PrithviMAE Enablement on vLLM v1 engine #20072

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[Core][Model] PrithviMAE Enablement on vLLM v1 engine #20072

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christian-pinto commented Jun 25, 2025 •

edited by github-actions bot

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christian-pinto Jun 26, 2025 •

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christian-pinto Jul 3, 2025 •

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christian-pinto Jul 7, 2025 •

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