# from triton.common.backend import compute_core_version_key
# elif triton_version.startswith("3.0"):
# from triton.compiler.compiler import triton_key
# else:
# print(f"TRITON version {triton_version} is not specifically handled.")
PROMPT="You are a helpful assistant in recognizes the content of tables in markdown format. Here is a table as fellows. You need to answer my question about the table.\n# Table\n|Opening|Opening|Sl. No.|Film|Cast|Director|Music Director|Notes|\n|----|----|----|----|----|----|----|----|\n|J A N|9|1|Agni Pushpam|Jayabharathi, Kamalahasan|Jeassy|M. K. Arjunan||\n|J A N|16|2|Priyamvada|Mohan Sharma, Lakshmi, KPAC Lalitha|K. S. Sethumadhavan|V. Dakshinamoorthy||\n|J A N|23|3|Yakshagaanam|Madhu, Sheela|Sheela|M. S. Viswanathan||\n|J A N|30|4|Paalkkadal|Sheela, Sharada|T. K. Prasad|A. T. Ummer||\n|F E B|5|5|Amma|Madhu, Srividya|M. Krishnan Nair|M. K. Arjunan||\n|F E B|13|6|Appooppan|Thikkurissi Sukumaran Nair, Kamal Haasan|P. Bhaskaran|M. S. Baburaj||\n|F E B|20|7|Srishti|Chowalloor Krishnankutty, Ravi Alummoodu|K. T. Muhammad|M. S. Baburaj||\n|F E B|20|8|Vanadevatha|Prem Nazir, Madhubala|Yusufali Kechery|G. Devarajan||\n|F E B|27|9|Samasya|Madhu, Kamalahaasan|K. Thankappan|Shyam||\n|F E B|27|10|Yudhabhoomi|K. P. Ummer, Vidhubala|Crossbelt Mani|R. K. Shekhar||\n|M A R|5|11|Seemantha Puthran|Prem Nazir, Jayabharathi|A. B. Raj|M. K. Arjunan||\n|M A R|12|12|Swapnadanam|Rani Chandra, Dr. Mohandas|K. G. George|Bhaskar Chandavarkar||\n|M A R|19|13|Thulavarsham|Prem Nazir, sreedevi, Sudheer|N. Sankaran Nair|V. Dakshinamoorthy||\n|M A R|20|14|Aruthu|Kaviyoor Ponnamma, Kamalahasan|Ravi|G. Devarajan||\n|M A R|26|15|Swimming Pool|Kamal Haasan, M. G. Soman|J. Sasikumar|M. K. Arjunan||\n\n# Question\nWhat' s the content in the (1,1) cells\n"# noqa: E501
PROMPT="You are a helpful assistant in recognizes the content of tables in markdown format. Here is a table as fellows. You need to answer my question about the table.\n# Table\n|Opening|Opening|Sl. No.|Film|Cast|Director|Music Director|Notes|\n|----|----|----|----|----|----|----|----|\n|J A N|9|1|Agni Pushpam|Jayabharathi, Kamalahasan|Jeassy|M. K. Arjunan||\n|J A N|16|2|Priyamvada|Mohan Sharma, Lakshmi, KPAC Lalitha|K. S. Sethumadhavan|V. Dakshinamoorthy||\n|J A N|23|3|Yakshagaanam|Madhu, Sheela|Sheela|M. S. Viswanathan||\n|J A N|30|4|Paalkkadal|Sheela, Sharada|T. K. Prasad|A. T. Ummer||\n|F E B|5|5|Amma|Madhu, Srividya|M. Krishnan Nair|M. K. Arjunan||\n|F E B|13|6|Appooppan|Thikkurissi Sukumaran Nair, Kamal Haasan|P. Bhaskaran|M. S. Baburaj||\n|F E B|20|7|Srishti|Chowalloor Krishnankutty, Ravi Alummoodu|K. T. Muhammad|M. S. Baburaj||\n|F E B|20|8|Vanadevatha|Prem Nazir, Madhubala|Yusufali Kechery|G. Devarajan||\n|F E B|27|9|Samasya|Madhu, Kamalahaasan|K. Thankappan|Shyam||\n|F E B|27|10|Yudhabhoomi|K. P. Ummer, Vidhubala|Crossbelt Mani|R. K. Shekhar||\n|M A R|5|11|Seemantha Puthran|Prem Nazir, Jayabharathi|A. B. Raj|M. K. Arjunan||\n|M A R|12|12|Swapnadanam|Rani Chandra, Dr. Mohandas|K. G. George|Bhaskar Chandavarkar||\n|M A R|19|13|Thulavarsham|Prem Nazir, sreedevi, Sudheer|N. Sankaran Nair|V. Dakshinamoorthy||\n|M A R|20|14|Aruthu|Kaviyoor Ponnamma, Kamalahasan|Ravi|G. Devarajan||\n|M A R|26|15|Swimming Pool|Kamal Haasan, M. G. Soman|J. Sasikumar|M. K. Arjunan||\n\n# Question\nWhat' s the content in the (1,1) cells\n"# noqa: E501
@@ -47,10 +47,6 @@ You can tune the performance by adjusting `max_num_batched_tokens`:
...
@@ -47,10 +47,6 @@ You can tune the performance by adjusting `max_num_batched_tokens`:
- For optimal throughput, we recommend setting `max_num_batched_tokens > 8192` especially for smaller models on large GPUs.
- For optimal throughput, we recommend setting `max_num_batched_tokens > 8192` especially for smaller models on large GPUs.
- If `max_num_batched_tokens` is the same as `max_model_len`, that's almost the equivalent to the V0 default scheduling policy (except that it still prioritizes decodes).
- If `max_num_batched_tokens` is the same as `max_model_len`, that's almost the equivalent to the V0 default scheduling policy (except that it still prioritizes decodes).
!!! warning
When chunked prefill is disabled, `max_num_batched_tokens` must be greater than `max_model_len`.
In that case, if `max_num_batched_tokens < max_model_len`, vLLM may crash at server start‑up.
```python
```python
fromvllmimportLLM
fromvllmimportLLM
...
@@ -289,4 +285,4 @@ Based on the configuration, the content of the multi-modal caches on `P0` and `P
...
@@ -289,4 +285,4 @@ Based on the configuration, the content of the multi-modal caches on `P0` and `P
| N/A | Disabled | N/A | N/A | N/A | `0` |
| N/A | Disabled | N/A | N/A | N/A | `0` |
K: Stores the hashes of multi-modal items
K: Stores the hashes of multi-modal items
V: Stores the processed tensor data of multi-modal items
V: Stores the processed tensor data of multi-modal items
@@ -145,4 +145,4 @@ Please see [`LinearAttentionMetadata`](../../../vllm/v1/attention/backends/linea
...
@@ -145,4 +145,4 @@ Please see [`LinearAttentionMetadata`](../../../vllm/v1/attention/backends/linea
It is also worth noting that we should update `MAMBA_TYPE_TO_BACKEND_MAP` and `MambaAttentionBackendEnum` in [`registry.py`](../../../vllm/v1/attention/backends/registry.py) when adding a new mamba backend.
It is also worth noting that we should update `MAMBA_TYPE_TO_BACKEND_MAP` and `MambaAttentionBackendEnum` in [`registry.py`](../../../vllm/v1/attention/backends/registry.py) when adding a new mamba backend.
Finally, if one wants to support torch compile and CUDA graphs, it necessary to wrap the call to the mamba-like layer inside a custom op and register it.
Finally, if one wants to support torch compile and CUDA graphs, it necessary to wrap the call to the mamba-like layer inside a custom op and register it.
Please see the calls to `direct_register_custom_op` in [vllm/model_executor/models/minimax_text_01.py](../../../vllm/model_executor/models/minimax_text_01.py) or [vllm/model_executor/layers/mamba/short_conv.py](../../../vllm/model_executor/layers/mamba/short_conv.py) for examples of this.
Please see the calls to `direct_register_custom_op` in [vllm/model_executor/models/minimax_text_01.py](../../../vllm/model_executor/models/minimax_text_01.py) or [vllm/model_executor/layers/mamba/short_conv.py](../../../vllm/model_executor/layers/mamba/short_conv.py) for examples of this.
The new custom op should then be added to the list `_attention_ops` in [vllm/config/compilation.py](../../../vllm/config/compilation.py) to ensure that piecewise CUDA graphs works as intended.
The new custom op should then be added to the list `_attention_ops` in [vllm/config/compilation.py](../../../vllm/config/compilation.py) to ensure that piecewise CUDA graphs works as intended.
@@ -43,73 +43,28 @@ Further update the model as follows:
...
@@ -43,73 +43,28 @@ Further update the model as follows:
)
)
```
```
- Remove the embedding part from the [forward][torch.nn.Module.forward] method:
- Implement [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal] that returns the embeddings from running the multimodal inputs through the multimodal tokenizer of the model. Below we provide a boilerplate of a typical implementation pattern, but feel free to adjust it to your own needs.
- Move the multi-modal embedding to [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal].
- The text embedding and embedding merge are handled automatically by a default implementation of [embed_input_ids][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_input_ids]. It does not need to be overridden in most cases.
Below we provide a boilerplate of a typical implementation pattern of [embed_multimodal][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal], but feel free to adjust it to your own needs.
The returned `multimodal_embeddings` must be either a **3D [torch.Tensor][]** of shape `(num_items, feature_size, hidden_size)`, or a **list / tuple of 2D [torch.Tensor][]'s** of shape `(feature_size, hidden_size)`, so that `multimodal_embeddings[i]` retrieves the embeddings generated from the `i`-th multimodal data item (e.g, image) of the request.
The returned `multimodal_embeddings` must be either a **3D [torch.Tensor][]** of shape `(num_items, feature_size, hidden_size)`, or a **list / tuple of 2D [torch.Tensor][]'s** of shape `(feature_size, hidden_size)`, so that `multimodal_embeddings[i]` retrieves the embeddings generated from the `i`-th multimodal data item (e.g, image) of the request.
@@ -10,7 +10,7 @@ receives a request for a LoRA adapter that hasn't been loaded yet, the resolver
...
@@ -10,7 +10,7 @@ receives a request for a LoRA adapter that hasn't been loaded yet, the resolver
to locate and load the adapter from their configured storage locations. This enables:
to locate and load the adapter from their configured storage locations. This enables:
-**Dynamic LoRA Loading**: Load adapters on-demand without server restarts
-**Dynamic LoRA Loading**: Load adapters on-demand without server restarts
-**Multiple Storage Backends**: Support for filesystem, S3, and custom backends. The built-in `lora_filesystem_resolver` requires a local storage path, while the built-in `hf_hub_resolver` will pull LoRA adapters from Huggingface Hub and proceed in an identical manner. In general, custom resolvers can be implemented to fetch from any source.
-**Multiple Storage Backends**: Support for filesystem, S3, and custom backends. The built-in `lora_filesystem_resolver` requires a local storage path, but custom resolvers can be implemented to fetch from any source.
-**Automatic Discovery**: Seamless integration with existing LoRA workflows
-**Automatic Discovery**: Seamless integration with existing LoRA workflows
-**Scalable Deployment**: Centralized adapter management across multiple vLLM instances
-**Scalable Deployment**: Centralized adapter management across multiple vLLM instances
...
@@ -217,4 +217,4 @@ To implement your own resolver plugin:
...
@@ -217,4 +217,4 @@ To implement your own resolver plugin:
| pplx | batched | fp8,int8 | G,A,T | Y | Y | [`PplxPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.pplx_prepare_finalize.PplxPrepareAndFinalize] |
| pplx | batched | fp8,int8 | G,A,T | Y | Y | [`PplxPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.pplx_prepare_finalize.PplxPrepareAndFinalize] |
| deepep_high_throughput | standard | fp8 | G(128),A,T<sup>2</sup> | Y | Y | [`DeepEPLLPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize.DeepEPLLPrepareAndFinalize] |
| deepep_high_throughput | standard | fp8 | G(128),A,T<sup>2</sup> | Y | Y | [`DeepEPLLPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize.DeepEPLLPrepareAndFinalize] |
| deepep_low_latency | batched | fp8 | G(128),A,T<sup>3</sup> | Y | Y | [`DeepEPHTPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize.DeepEPHTPrepareAndFinalize] |
| deepep_low_latency | batched | fp8 | G(128),A,T<sup>3</sup> | Y | Y | [`DeepEPHTPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize.DeepEPHTPrepareAndFinalize] |
| flashinfer_all2allv | standard | nvfp4,fp8 | G,A,T | N | N | [`FlashInferA2APrepareAndFinalize`][vllm.model_executor.layers.fused_moe.flashinfer_a2a_prepare_finalize.FlashInferA2APrepareAndFinalize] |
| flashinfer_all2allv | standard | nvfp4,fp8 | G,A,T | N | N | [`FlashInferAllToAllMoEPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize.FlashInferAllToAllMoEPrepareAndFinalize] |
| flashinfer<sup>4</sup> | standard | nvfp4,fp8 | G,A,T | N | N | [`FlashInferCutlassMoEPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize.FlashInferCutlassMoEPrepareAndFinalize] |
| MoEPrepareAndFinalizeNoEP<sup>5</sup> | standard | fp8,int8 | G,A,T | N | Y | [`MoEPrepareAndFinalizeNoEP`][vllm.model_executor.layers.fused_moe.prepare_finalize.MoEPrepareAndFinalizeNoEP] |
| MoEPrepareAndFinalizeNoEP<sup>5</sup> | standard | fp8,int8 | G,A,T | N | Y | [`MoEPrepareAndFinalizeNoEP`][vllm.model_executor.layers.fused_moe.prepare_finalize.MoEPrepareAndFinalizeNoEP] |
| BatchedPrepareAndFinalize<sup>5</sup> | batched | fp8,int8 | G,A,T | N | Y | [`BatchedPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.fused_batched_moe.BatchedPrepareAndFinalize] |
| BatchedPrepareAndFinalize<sup>5</sup> | batched | fp8,int8 | G,A,T | N | Y | [`BatchedPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.fused_batched_moe.BatchedPrepareAndFinalize] |
...
@@ -111,4 +112,4 @@ The following table shows "families" of modular kernels that are intended to wor
...
@@ -111,4 +112,4 @@ The following table shows "families" of modular kernels that are intended to wor
@@ -159,12 +159,10 @@ Alternatively, you can use the LoRAResolver plugin to dynamically load LoRA adap
...
@@ -159,12 +159,10 @@ Alternatively, you can use the LoRAResolver plugin to dynamically load LoRA adap
You can set up multiple LoRAResolver plugins if you want to load LoRA adapters from different sources. For example, you might have one resolver for local files and another for S3 storage. vLLM will load the first LoRA adapter that it finds.
You can set up multiple LoRAResolver plugins if you want to load LoRA adapters from different sources. For example, you might have one resolver for local files and another for S3 storage. vLLM will load the first LoRA adapter that it finds.
You can either install existing plugins or implement your own. By default, vLLM comes with a [resolver plugin to load LoRA adapters from a local directory, as well as a resolver plugin to load LoRA adapters from repositories on Hugging Face Hub](https://github.com/vllm-project/vllm/tree/main/vllm/plugins/lora_resolvers)
You can either install existing plugins or implement your own. By default, vLLM comes with a [resolver plugin to load LoRA adapters from a local directory.](https://github.com/vllm-project/vllm/tree/main/vllm/plugins/lora_resolvers)
To enable either of these resolvers, you must `set VLLM_ALLOW_RUNTIME_LORA_UPDATING` to True.
To enable this resolver, set `VLLM_ALLOW_RUNTIME_LORA_UPDATING` to True, set `VLLM_PLUGINS` to include `lora_filesystem_resolver`, and then set `VLLM_LORA_RESOLVER_CACHE_DIR` to a local directory. When vLLM receives a request using a LoRA adapter `foobar`,
it will first look in the local directory for a directory `foobar`, and attempt to load the contents of that directory as a LoRA adapter. If successful, the request will complete as normal and
- To leverage a local directory, set `VLLM_PLUGINS` to include `lora_filesystem_resolver` and set `VLLM_LORA_RESOLVER_CACHE_DIR` to a local directory. When vLLM receives a request using a LoRA adapter `foobar`,
that adapter will then be available for normal use on the server.
it will first look in the local directory for a directory `foobar`, and attempt to load the contents of that directory as a LoRA adapter. If successful, the request will complete as normal and that adapter will then be available for normal use on the server.
- To leverage repositories on Hugging Face Hub, set `VLLM_PLUGINS` to include `lora_hf_hub_resolver` and set `VLLM_LORA_RESOLVER_HF_REPO_LIST` to a comma separated list of repository IDs on Hugging Face Hub. When vLLM receives a request for the LoRA adapter `my/repo/subpath`, it will download the adapter at the `subpath` of `my/repo` if it exists and contains an `adapter_config.json`, then build a request to the cached dir for the adapter, similar to the `lora_filesystem_resolver`. Please note that enabling remote downloads is insecure and not intended for use in production environments.
Alternatively, follow these example steps to implement your own plugin:
Alternatively, follow these example steps to implement your own plugin:
...
@@ -387,4 +385,4 @@ vllm serve model --enable-lora --max-lora-rank 64
...
@@ -387,4 +385,4 @@ vllm serve model --enable-lora --max-lora-rank 64
@@ -675,7 +675,6 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
...
@@ -675,7 +675,6 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `GLM4VForCausalLM`<sup>^</sup> | GLM-4V | T + I | `zai-org/glm-4v-9b`, `zai-org/cogagent-9b-20241220`, etc. | ✅︎ | ✅︎ |
| `GLM4VForCausalLM`<sup>^</sup> | GLM-4V | T + I | `zai-org/glm-4v-9b`, `zai-org/cogagent-9b-20241220`, etc. | ✅︎ | ✅︎ |
| `Glm4vForConditionalGeneration` | GLM-4.1V-Thinking | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.1V-9B-Thinking`, etc. | ✅︎ | ✅︎ |
| `Glm4vForConditionalGeneration` | GLM-4.1V-Thinking | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.1V-9B-Thinking`, etc. | ✅︎ | ✅︎ |
| `Glm4vMoeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | ✅︎ | ✅︎ |
| `Glm4vMoeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | ✅︎ | ✅︎ |
| `GlmOcrForConditionalGeneration` | GLM-OCR | T + I<sup>E+</sup> | `zai-org/GLM-OCR`, etc. | ✅︎ | ✅︎ |
| `GraniteSpeechForConditionalGeneration` | Granite Speech | T + A | `ibm-granite/granite-speech-3.3-8b` | ✅︎ | ✅︎ |
| `GraniteSpeechForConditionalGeneration` | Granite Speech | T + A | `ibm-granite/granite-speech-3.3-8b` | ✅︎ | ✅︎ |
| `H2OVLChatModel` | H2OVL | T + I<sup>E+</sup> | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc. | | ✅︎ |
| `H2OVLChatModel` | H2OVL | T + I<sup>E+</sup> | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc. | | ✅︎ |
| `HunYuanVLForConditionalGeneration` | HunyuanOCR | T + I<sup>E+</sup> | `tencent/HunyuanOCR`, etc. | ✅︎ | ✅︎ |
| `HunYuanVLForConditionalGeneration` | HunyuanOCR | T + I<sup>E+</sup> | `tencent/HunyuanOCR`, etc. | ✅︎ | ✅︎ |
...
@@ -849,4 +848,4 @@ We have the following levels of testing for models:
...
@@ -849,4 +848,4 @@ We have the following levels of testing for models:
1.**Strict Consistency**: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy decoding. This is the most stringent test. Please refer to [models tests](https://github.com/vllm-project/vllm/blob/main/tests/models) for the models that have passed this test.
1.**Strict Consistency**: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy decoding. This is the most stringent test. Please refer to [models tests](https://github.com/vllm-project/vllm/blob/main/tests/models) for the models that have passed this test.
2.**Output Sensibility**: We check if the output of the model is sensible and coherent, by measuring the perplexity of the output and checking for any obvious errors. This is a less stringent test.
2.**Output Sensibility**: We check if the output of the model is sensible and coherent, by measuring the perplexity of the output and checking for any obvious errors. This is a less stringent test.
3.**Runtime Functionality**: We check if the model can be loaded and run without errors. This is the least stringent test. Please refer to [functionality tests](../../tests) and [examples](../../examples) for the models that have passed this test.
3.**Runtime Functionality**: We check if the model can be loaded and run without errors. This is the least stringent test. Please refer to [functionality tests](../../tests) and [examples](../../examples) for the models that have passed this test.
4.**Community Feedback**: We rely on the community to provide feedback on the models. If a model is broken or not working as expected, we encourage users to raise issues to report it or open pull requests to fix it. The rest of the models fall under this category.
4.**Community Feedback**: We rely on the community to provide feedback on the models. If a model is broken or not working as expected, we encourage users to raise issues to report it or open pull requests to fix it. The rest of the models fall under this category.
