[Documentation][torch.compile] Add documentation for torch.compile + multimodal encoders (#31627)

Signed-off-by: Lucas Kabela <lucaskabela@meta.com>

docs/design/torch_compile_multimodal.md

+# torch.compile with Multimodal Encoders
+
+`torch.compile` can now be applied to multimodal encoders and miscellaneous nn modules in vLLM, including vision-language models like LLaMA 4, Qwen-VL,
+and similar encoder-based architectures.
+
+This document covers the basics of how the `torch.compile` integration works for multimodal encoders in vLLM, as well as how to apply the decorator
+to new models to improve performance.
+
+!!! note
+    For general information about `torch.compile` integration in vLLM, see the [torch.compile design document](./torch_compile.md).
+
+## Overview
+
+We have recently enabled the `@supports_torch_compile` decorator to work for multiple nn module components within a model type; this enables
+turning compile on for multimodal encoders, bringing performance improvements to additional components of the stack.
+
+When applied to the vision block of [`Qwen2_5_vl`](https://github.com/vllm-project/vllm/pull/23207) we observe ~4.5% e2e perf improvements with
+some increase in compilation time
+
+This feature is off by default, but can be enabled by setting `compile_mm_encoder: true` in the compilation config when models have the
+`@supports_torch_compile` decorator.
+
+## How Compilation Works for Multimodal Components
+
+### APIs for Enablement
+
+To compile a multimodal component such as an encoder, we follow the same mechanism as the LLM text backbone, with a few additional scaffoldings:
+
+1. The `@supports_torch_compile` decorator should include `enable_if=should_torch_compile_mm_vit`. This will gate the compilation behind our
+`compile_mm_encoder` configuration
+
+2. `with set_model_tag("<component_name>", is_encoder=True)` context manager should be used around the nn.Module's instantiation. Since torch.compile
+relies on caching artifacts to reduce start time, we must properly propagate the `<component_name>` information to the cache in order to avoid collisions
+with the LLM text-backbone, or other instances of the same artifact (as is the case with vision block). `is_encoder=True` is also needed for encoder
+components (see Compile Range Integration).
+
+3. `with set_forward_context` context manager should be used around the nn.Module's forward call. This will properly forward the vllm_config which is needed
+for torch.compile integration.
+
+### CompilationConfig
+
+With the exception of `compile_mm_encoder: true`, the multimodal encoder will inherit from the same compilation config as the text LLM. We may extend
+this for more configuration in the future.
+
+## Applying torch.compile to a New Multimodal Model/Component
+
+To apply `supports_torch_compile` to a new general nn.Module, we advise following the same steps in [`debug_vllm_compile`](./debug_vllm_compile.md); this includes:
+
+1. Applying `supports_torch_compile` on initially small modules (such as basic MLP layers), then raising to more general modules until one reaches a good performance
+tradeoff
+
+2. Leveraging [`tlparse`](https://github.com/meta-pytorch/tlparse) to identify and eliminate the source of recompiles and graph breaks
+
+3. Using `dynamic_arg_dims` and proper `dynamic_shapes_config` to handle dynamism.
+
+### Common pitfalls
+
+## VllmBackend Feature Support
+
+### Compile ranges
+
+The torch.compile integration will try to rely on max_batch_size to infer compilation ranges for dynamic shapes; however, for modules used in the encoder, this
+shape can be difficult to infer due to the unspecified range of shapes the encoder may see as input. Therefore, we rely on `is_encoder=True` in the `set_model_tag`
+to alert torch.compile to the fact that this range cannot be inferred, and we default to the range (1, MAX_INT).
+
+!!! note
+    We may seek to tighten this range for better performance in the future
+
+### Cudagraphs
+
+We have not yet explored compilation for multimodal encoders with CUDAGraph integration; behavior is currently unspecified.
+
+## Troubleshooting
+
+### Graph Breaks in Vision Encoders
+
+Some vision encoder operations may cause graph breaks. To identify them:
+
+```bash
+TORCH_LOGS="+dynamo" vllm serve <MODEL>
+```
+
+Common causes of graph breaks in multimodal models:
+
+- **Dynamic image sizes**: Use `dynamic_shapes_config` to handle variable resolutions
+- **Untraceable operations**: Some operations (such as to_list) may not be supported by Dynamo
+- **Conditional processing**: Data-dependent branching based on image properties
+
+### Compilation Errors
+
+If compilation fails for a multimodal model:
+
+1. **Disable and test**: First verify the model works without compilation:
+   ```bash
+   VLLM_TORCH_COMPILE_LEVEL=0 vllm serve <model> --compilation-config='{"compile_mm_encoder":"false"}'
+   ```
+
+2. **Check logs**: Enable debug logging to see compilation details:
+   ```bash
+   VLLM_LOGGING_LEVEL=DEBUG vllm serve <model> --compilation-config='{"compile_mm_encoder":"true"}'
+   ```
+
+3. **Report issues**: If you find a bug, [open an issue on GitHub](https://github.com/vllm-project/vllm/issues/new/choose)
+
+## See Also
+
+- [torch.compile Integration](./torch_compile.md) - Core design document
+- [Debugging torch.compile](./debug_vllm_compile.md) - Detailed debugging guide
+- [Multimodal Inputs](../features/multimodal_inputs.md) - How to pass multimodal data
+- [Disaggregated Encoder](../features/disagg_encoder.md) - Scaling vision encoders
+- [Supported Multimodal Models](../models/supported_models.md#list-of-multimodal-language-models) - Model compatibility