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VitisAI EP Context Model (microsoft#20926)
# Why so many commits - Runtime debugging - which is necessary - Three different approaches to EP context model - as a result testing back and forth - Windows compatibility issues - this development has been done on Linux for convenience # "Open" (?) questions - Full offloading to a specific EP - Dumping EP context models by EPs vs [by ONNXRT](https://github.com/microsoft/onnxruntime/blob/e2abba18ea9370329ce6894a4eb3e98ad8f11cb6/onnxruntime/core/framework/graph_partitioner.cc#L725) - [Node name to pick nodes](https://github.com/microsoft/onnxruntime/blob/e2abba18ea9370329ce6894a4eb3e98ad8f11cb6/onnxruntime/core/framework/graph_partitioner.cc#L654) # VitisAI EP made three variant implementations that have respective pros and cons (and of course we can combine them) ## Serialize and cache the list of compute capabilities and the original ONNX model itself ## In `ComputeCapability()`, serialize and cache the backend compilation cache and the related necessary cache info such as cache dir and cache key ## In `Compile()`, serialize and cache the backend compilation cache and the related necessary cache info such as cache dir and cache key # EP context model creation - Precondition Session option configuration `kOrtSessionOptionEpContextEnable` (aka "ep.context_enable") is enabled. - Approach 1 - Steps 1. EP creates an ONNX model whose main graph has EP context nodes (i.e., node type is "EPContext"). 2. EP implements/overrides `IExecutionProvider::GetEpContextNodes()` method. 3. ONNXRT core creates an EP context model and saves/dumps it. - `CreateEpContextModel()` in the file "graph_partitioner.cc" - 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()`. - The workaround is (1) not implementing `IExecutionProvider::GetEpContextNodes()` and (2) dumping the EP context model by EP itself. 4. Optionally, EP can also dump the EP context model it created by iteself. - Examples - `QNNExecutionProvider` - `VitisAIExecutionProvider` - Approach 2 - Steps 1. EP creates an ONNX model whose main graph has EP context nodes (i.e., node type is "EPContext"). 2. EP does NOT implement `IExecutionProvider::GetEpContextNodes()` at all. 3. EP dumps the EP context model it created. - Examples - `TensorrtExecutionProvider` - UPDATES - TRT EP is switching to leveraging `IExecutionProvider::GetEpContextNodes()` - `OpenVINOExecutionProvider` (?) # What to cache in EP context nodes - Non Compilation based EPs - Examples - `VitisAIExecutionProvider` - Characteristics - Heavy lifting work happens in `IExecutionProvider::GetCapability()`. - Preconditions - `IExecutionProvider::GetCapability()` is only called once by ONNXRT. - Cache content - Serialization of a list of `ComputeCapability` - Not EP-specific - Serialized using `onnx::FunctionProto` - EP-specific cache - Compilation based EPs - Examples - `QNNExecutionProvider` - `TensorrtExecutionProvider` - `MIGraphXExecutionProvider` - `OpenVINOExecutionProvider` - Cache content - EP-specific cache # Requirements - Offline / AOT compilation of ONNX models with EP context cache - Compile somewhere, run everywhere - Pseudo code with brief explanation ``` GenerateCache(original_onnx_file, cache_onnx_file) model_buffer = load(original_onnx_file) --> Load the original ONNX model file model_buffer = decrypt(model_buffer) session_options = { kOrtSessionOptionEpContextEnable: true, kOrtSessionOptionEpContextFilePath: temp_file } --> Set necessary configs Ort::CreateSessionFromArray(model_buffer, session_options) --> The new ONNX model with EP context is created and dumped into the user specified file "temp_file" temp_buffer = encrypt(temp_file) write(temp_buffer, cache_onnx_file) --> Write the encypted context of "temp_file" into the "cache_onnx_file" file InitializeInferenceSession(cache_onnx_file) model_buffer = load(cache_onnx_file) --> Load the ONNX model with EP context from the file generated in the previous step model_buffer = decrypt(model_buffer) session_options = { } Ort::CreateSessionFromArray(model_buffer, session_options) --> Create and initalize an session with the EP context model ``` - Python code with comments - EP context model creation ```python import onnxruntime as onnxrt # Session options for creating an ONNX model with EP context cache. sess_opts = onnxrt.SessionOptions() # Verbose. sess_opts.log_severity_level = 0 # This is REQUIRED. sess_opts.add_session_config_entry("ep.context_enable", "1") # This is OPTIONAL. # Either an absolute path (preferred for now) or a relative path (WIP) is okay. # sess_opts.add_session_config_entry("ep.context_file_path", "/some/path/to/original_model_ctx.onnx") # This is OPTIONAL. sess_opts.add_session_config_entry("ep.context_embed_mode", "1") orig_model_location = "/some/path/to/original_model.onnx" sess = onnxrt.InferenceSession(orig_model_location, sess_opts, providers=["VitisAIExecutionProvider"], provider_options=[]) ``` - Inference run with an EP context model ```python import onnxruntime as onnxrt # Session options for creating an ONNX model with EP context cache. sess_opts = onnxrt.SessionOptions() # Default EP context model path. # ep_ctx_model_location = "/some/path/to/origina_model.onnx_ctx.onnx" # User configured EP context model path. ep_ctx_model_location = "/some/path/to/origina_model_ctx.onnx" sess = onnxrt.InferenceSession(ep_ctx_model_location, sess_opts, providers=["VitisAIExecutionProvider"], provider_options=[]) model_inputs = {} run_opts = onnxrt.RunOptions() # Verbose. run_opts.log_severity_level = 1 sess.run(None, model_inputs, run_opts) ``` --------- Co-authored-by: Glen Cao <[email protected]>
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