{"payload":{"feedbackUrl":"https://github.com/orgs/community/discussions/53140","repo":{"id":156939672,"defaultBranch":"main","name":"onnxruntime","ownerLogin":"microsoft","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2018-11-10T02:22:53.000Z","ownerAvatar":"https://github.com/avatars/u/6154722?v=4","public":true,"private":false,"isOrgOwned":true},"refInfo":{"name":"","listCacheKey":"v0:1720826463.0","currentOid":""},"activityList":{"items":[{"before":"96241e5e3beee2e2e95b709708e787219f025602","after":"a8ea17ed88768ac4cacb6ffd2177b59b73a05ab8","ref":"refs/heads/sajandhy/webgpu_support_64_bit_integer","pushedAt":"2024-07-13T05:46:16.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"satyajandhyala","name":"Satya Kumar Jandhyala","path":"/satyajandhyala","primaryAvatarUrl":"https://github.com/avatars/u/26722914?s=80&v=4"},"commit":{"message":"Clean up","shortMessageHtmlLink":"Clean up"}},{"before":"92a8407b392bf87f2d0d0d556a2eb6dfe2929eef","after":"281ed8c12d2d2a3f5b683e6267aa0fca4d4add50","ref":"refs/heads/main","pushedAt":"2024-07-13T04:22:58.000Z","pushType":"pr_merge","commitsCount":1,"pusher":{"login":"HectorSVC","name":"Hector Li","path":"/HectorSVC","primaryAvatarUrl":"https://github.com/avatars/u/29932710?s=80&v=4"},"commit":{"message":"VitisAI EP Context Model (#20926)\n\n# Why so many commits\r\n- Runtime debugging - which is necessary\r\n- Three different approaches to EP context model - as a result testing back and forth\r\n- Windows compatibility issues - this development has been done on Linux for convenience\r\n\r\n# \"Open\" (?) questions\r\n- Full offloading to a specific EP\r\n- Dumping EP context models by EPs vs [by\r\nONNXRT](https://github.com/microsoft/onnxruntime/blob/e2abba18ea9370329ce6894a4eb3e98ad8f11cb6/onnxruntime/core/framework/graph_partitioner.cc#L725)\r\n- [Node name to pick\r\nnodes](https://github.com/microsoft/onnxruntime/blob/e2abba18ea9370329ce6894a4eb3e98ad8f11cb6/onnxruntime/core/framework/graph_partitioner.cc#L654)\r\n\r\n# VitisAI EP made three variant implementations that have respective pros and cons (and of course we can combine them)\r\n## Serialize and cache the list of compute capabilities and the original\r\nONNX model itself\r\n## In `ComputeCapability()`, serialize and cache the backend compilation cache and the related necessary cache info such as cache dir and cache key\r\n## In `Compile()`, serialize and cache the backend compilation cache and the related necessary cache info such as cache dir and cache key\r\n\r\n# EP context model creation\r\n- Precondition\r\nSession option configuration `kOrtSessionOptionEpContextEnable` (aka \"ep.context_enable\") is enabled.\r\n- Approach 1\r\n  - Steps\r\n1. EP creates an ONNX model whose main graph has EP context nodes (i.e., node type is \"EPContext\").\r\n2. EP implements/overrides `IExecutionProvider::GetEpContextNodes()` method.\r\n    3. ONNXRT core creates an EP context model and saves/dumps it.\r\n       - `CreateEpContextModel()` in the file \"graph_partitioner.cc\"\r\n- In `get_ep_context_node()`, `Node::Name()` is used to check whether a node is an EP context node. This limits that EP model creation can only happen in `IExecutionProvider::Compile()`.\r\n- The workaround is (1) not implementing `IExecutionProvider::GetEpContextNodes()` and (2) dumping the EP context model by EP itself.\r\n4. Optionally, EP can also dump the EP context model it created by\r\niteself.\r\n  - Examples\r\n    - `QNNExecutionProvider`\r\n    - `VitisAIExecutionProvider`\r\n- Approach 2\r\n  - Steps\r\n1. EP creates an ONNX model whose main graph has EP context nodes (i.e., node type is \"EPContext\").\r\n2. EP does NOT implement `IExecutionProvider::GetEpContextNodes()` at all.\r\n    3. EP dumps the EP context model it created.\r\n  - Examples\r\n    - `TensorrtExecutionProvider`\r\n       - UPDATES\r\n- TRT EP is switching to leveraging\r\n`IExecutionProvider::GetEpContextNodes()`\r\n    - `OpenVINOExecutionProvider` (?)\r\n\r\n# What to cache in EP context nodes\r\n- Non Compilation based EPs\r\n  - Examples\r\n    - `VitisAIExecutionProvider`\r\n  - Characteristics\r\n- Heavy lifting work happens in `IExecutionProvider::GetCapability()`.\r\n  - Preconditions\r\n- `IExecutionProvider::GetCapability()` is only called once by ONNXRT.\r\n  - Cache content\r\n    - Serialization of a list of `ComputeCapability`\r\n      - Not EP-specific\r\n      - Serialized using `onnx::FunctionProto`\r\n    - EP-specific cache\r\n- Compilation based EPs\r\n  - Examples\r\n    - `QNNExecutionProvider`\r\n    - `TensorrtExecutionProvider`\r\n    - `MIGraphXExecutionProvider`\r\n    - `OpenVINOExecutionProvider`\r\n  - Cache content\r\n    - EP-specific cache\r\n\r\n# Requirements\r\n- Offline / AOT compilation of ONNX models with EP context cache\r\n- Compile somewhere, run everywhere\r\n- Pseudo code with brief explanation\r\n  ```\r\n  GenerateCache(original_onnx_file, cache_onnx_file) model_buffer = load(original_onnx_file) --> Load the original ONNX model file\r\n    model_buffer = decrypt(model_buffer)\r\nsession_options = { kOrtSessionOptionEpContextEnable: true,\r\nkOrtSessionOptionEpContextFilePath: temp_file } --> Set necessary configs\r\nOrt::CreateSessionFromArray(model_buffer, session_options) --> The new ONNX model with EP context is created and dumped into the user specified file \"temp_file\"\r\n    temp_buffer = encrypt(temp_file)\r\nwrite(temp_buffer, cache_onnx_file) --> Write the encypted context of \"temp_file\" into the \"cache_onnx_file\" file\r\n\r\n\r\n  InitializeInferenceSession(cache_onnx_file)\r\nmodel_buffer = load(cache_onnx_file) --> Load the ONNX model with EP context from the file generated in the previous step\r\n    model_buffer = decrypt(model_buffer)\r\n    session_options = { }\r\nOrt::CreateSessionFromArray(model_buffer, session_options) --> Create and initalize an session with the EP context model\r\n  ```\r\n- Python code with comments\r\n  - EP context model creation\r\n    ```python\r\n    import onnxruntime as onnxrt\r\n\r\n\r\n    # Session options for creating an ONNX model with EP context cache.\r\n    sess_opts = onnxrt.SessionOptions()\r\n\r\n    # Verbose.\r\n    sess_opts.log_severity_level = 0\r\n\r\n    # This is REQUIRED.\r\n    sess_opts.add_session_config_entry(\"ep.context_enable\", \"1\")\r\n    # This is OPTIONAL.\r\n# Either an absolute path (preferred for now) or a relative path (WIP)\r\nis okay.\r\n# sess_opts.add_session_config_entry(\"ep.context_file_path\",\r\n\"/some/path/to/original_model_ctx.onnx\")\r\n    # This is OPTIONAL.\r\n    sess_opts.add_session_config_entry(\"ep.context_embed_mode\", \"1\")\r\n\r\n    orig_model_location = \"/some/path/to/original_model.onnx\"\r\nsess = onnxrt.InferenceSession(orig_model_location, sess_opts,\r\nproviders=[\"VitisAIExecutionProvider\"], provider_options=[])\r\n    ```\r\n  - Inference run with an EP context model\r\n    ```python\r\n    import onnxruntime as onnxrt\r\n\r\n\r\n    # Session options for creating an ONNX model with EP context cache.\r\n    sess_opts = onnxrt.SessionOptions()\r\n\r\n    # Default EP context model path.\r\n# ep_ctx_model_location = \"/some/path/to/origina_model.onnx_ctx.onnx\"\r\n    # User configured EP context model path.\r\n    ep_ctx_model_location = \"/some/path/to/origina_model_ctx.onnx\"\r\nsess = onnxrt.InferenceSession(ep_ctx_model_location, sess_opts,\r\nproviders=[\"VitisAIExecutionProvider\"], provider_options=[])\r\n\r\n    model_inputs = {}\r\n    run_opts = onnxrt.RunOptions()\r\n    # Verbose.\r\n    run_opts.log_severity_level = 1\r\n    sess.run(None, model_inputs, run_opts)\r\n    ```\r\n\r\n---------\r\n\r\nCo-authored-by: Glen Cao 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