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Add comprehensive documentation for ATOM's vLLM OOT plugin mode covering architecture, supported models, configuration translation, attention integration, model wrapper, environment variables, and troubleshooting. Signed-off-by: wuhuikx <hattie.wu@amd.com>
| > | Component | Import / Entry Point | Purpose | | ||
| > |-----------|---------------------|---------| | ||
| > | `register_platform` | `atom.plugin.vllm.register:register_platform` | vLLM platform plugin — returns `ATOMPlatform` | | ||
| > | `register_model` | `atom.plugin.vllm.register:register_model` | vLLM general plugin — overrides model registry | |
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For the function register_platform and register_model, we need to add more descriptions for both functions because they are the entrance of the ATOM OOT plugin of vLLM. https://github.com/ROCm/ATOM/blob/main/pyproject.toml#L33-L39
| ## 1. Overview | ||
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| When ATOM is installed alongside vLLM, it automatically registers itself via | ||
| Python entry points. vLLM discovers ATOM at startup and delegates model |
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| Python entry points. vLLM discovers ATOM at startup and delegates model | |
| Python entry points when launching the vLLM server. vLLM discovers ATOM at startup and delegates model |
| | `Qwen3MoeForCausalLM` | `atom.models.qwen3_moe.Qwen3MoeForCausalLM` | `ATOMMoEForCausalLM` | MoE | | ||
| | `DeepseekV3ForCausalLM` | `atom.models.deepseek_v2.DeepseekV3ForCausalLM` | `ATOMMoEForCausalLM` | MoE | | ||
| | `GptOssForCausalLM` | `atom.models.gpt_oss.GptOssForCausalLM` | `ATOMMoEForCausalLM` | MoE | | ||
| | `Glm4MoeForCausalLM` | `atom.models.glm4_moe.Glm4MoeForCausalLM` | `ATOMMoEForCausalLM` | MoE | |
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Kimi is also supported but shares the same architecture with DS. Here we can tell to users that Kimi-K2 is supported.
| If you see `Fallback to original vLLM attention backend`, the attention plugin | ||
| is disabled. | ||
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| ### Illegal memory access in MoE |
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We can remove this section about illegal mem access issue because the issue is gone
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Motivation
Add comprehensive documentation for ATOM's vLLM OOT plugin mode covering
architecture, supported models, configuration translation, attention
integration, model wrapper, environment variables, and troubleshooting.