graph LR
tvm_ffi_registry["tvm.ffi.registry"]
tvm_contrib_msc_pipeline_pipeline["tvm.contrib.msc.pipeline.pipeline"]
tvm_contrib_msc_pipeline_wrapper["tvm.contrib.msc.pipeline.wrapper"]
tvm_contrib_msc_pipeline_worker["tvm.contrib.msc.pipeline.worker"]
tvm_contrib_msc_core_tools_tool["tvm.contrib.msc.core.tools.tool"]
tvm_contrib_msc_core_runtime_runner["tvm.contrib.msc.core.runtime.runner"]
tvm_topi_nn_conv2d["tvm.topi.nn.conv2d"]
tvm_topi_nn_utils["tvm.topi.nn.utils"]
tvm_contrib_msc_pipeline_wrapper -- "initiates" --> tvm_contrib_msc_pipeline_pipeline
tvm_contrib_msc_pipeline_pipeline -- "orchestrates" --> tvm_contrib_msc_pipeline_worker
tvm_contrib_msc_pipeline_worker -- "interacts with" --> tvm_contrib_msc_core_tools_tool
tvm_contrib_msc_pipeline_worker -- "interacts with" --> tvm_contrib_msc_core_runtime_runner
tvm_contrib_msc_core_runtime_runner -- "relies on" --> tvm_ffi_registry
tvm_topi_nn_conv2d -- "utilizes" --> tvm_topi_nn_utils
The Python Control Plane & Extensions subsystem serves as the primary user-facing interface and orchestration layer, enabling Python to control the entire TVM compilation process via Foreign Function Interface (FFI). It also includes advanced model scaling optimizations (MSC) and a library of optimized tensor operators (TOPI).
Serves as the core Foreign Function Interface (FFI) bridge, enabling seamless invocation of C++ functions from Python and vice-versa. It is fundamental to the "Python as Control Plane" architectural bias, allowing Python to orchestrate the C++ backend.
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The central orchestrator for the entire Model Scaling Optimization (MSC) framework. It defines and executes the multi-stage model optimization and compilation process, embodying the "Compilation Flow Emphasis" pattern. It manages the flow from model parsing to final summary.
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Provides a simplified, user-friendly Pythonic interface to the complex MSC pipeline, abstracting away internal complexities for end-users. This component enhances the "Python as Control Plane" by making the optimization pipeline accessible.
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Executes specific tasks and applies various optimization and analysis tools within the MSC pipeline stages. It acts as a delegated processing unit for the main pipeline orchestrator, supporting the modularity of the optimization flow.
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Defines a standardized interface and common functionalities for all MSC optimization and analysis tools (e.g., quantization, pruning). This promotes extensibility and aligns with the "Plugin/Extension" architectural pattern.
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Manages the lifecycle of models at runtime, including building, executing, and exporting compiled models. This component highlights the distinction between "Runtime vs. Compile-time" within the MSC framework.
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Implements a highly optimized 2D convolution operation, a fundamental building block in deep learning. It showcases how Python extensions (TOPI) provide performance-critical tensor operations, contributing to the "Extensibility for Hardware Backends" by offering optimized primitives.
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Provides a collection of common utility functions used across various neural network operators within TOPI, supporting efficient tensor operations and promoting code reuse within the extension library.
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