graph LR
deepspeed_accelerator_real_accelerator["deepspeed.accelerator.real_accelerator"]
deepspeed_accelerator_concrete_accelerators["deepspeed.accelerator.concrete_accelerators"]
deepspeed_ops_op_builder_builder_OpBuilder["deepspeed.ops.op_builder.builder.OpBuilder"]
deepspeed_ops_op_builder_concrete_op_builders["deepspeed.ops.op_builder.concrete_op_builders"]
deepspeed_accelerator_real_accelerator -- "provides instances of" --> deepspeed_accelerator_concrete_accelerators
deepspeed_accelerator_concrete_accelerators -- "requests kernels from" --> deepspeed_ops_op_builder_builder_OpBuilder
deepspeed_ops_op_builder_builder_OpBuilder -- "relies on" --> deepspeed_ops_op_builder_concrete_op_builders
deepspeed_ops_op_builder_builder_OpBuilder -- "provides kernels to" --> deepspeed_accelerator_concrete_accelerators
deepspeed_ops_op_builder_concrete_op_builders -- "configures" --> deepspeed_ops_op_builder_builder_OpBuilder
The DeepSpeed acceleration subsystem is designed to provide a flexible and efficient interface for interacting with diverse hardware environments and custom operations. At its core, deepspeed.accelerator.real_accelerator acts as a dynamic factory, abstracting away hardware specifics by providing the appropriate deepspeed.accelerator.concrete_accelerators instance based on the detected environment. These concrete accelerators manage hardware-specific details and, crucially, interact with deepspeed.ops.op_builder.builder.OpBuilder. The OpBuilder is responsible for the Just-In-Time (JIT) compilation and loading of highly optimized custom kernels. It achieves this by leveraging deepspeed.ops.op_builder.concrete_op_builders, which are specialized blueprints defining the compilation requirements for individual operations. This architecture ensures that DeepSpeed can seamlessly adapt to various hardware backends while dynamically integrating performance-critical custom operations.
Acts as a central factory or registry responsible for detecting the current hardware environment (e.g., NVIDIA GPU, Intel HPU) and providing the appropriate hardware-specific accelerator instance. This ensures DeepSpeed can seamlessly integrate with various hardware backends.
Related Classes/Methods:
These are concrete implementations of the hardware abstraction layer. Each class encapsulates the specific logic for interacting with a particular type of accelerator, including memory management, device synchronization, data type handling, and the creation/retrieval of custom operations. They provide a consistent interface for DeepSpeed's core logic.
Related Classes/Methods:
This is the core component responsible for dynamically compiling and loading custom C++/CUDA/HIP operations (kernels) at runtime. It manages the complexities of compiler arguments, environment checks, and Just-In-Time (JIT) compilation. This is fundamental for DeepSpeed's "Optimization Techniques" and "Low-level Performance."
Related Classes/Methods:
These specialized builder classes define the specific source files, include paths, and compiler arguments required for individual custom operations or hardware-specific kernels. They serve as blueprints for OpBuilder, encapsulating the details of particular "Optimization Techniques" and enabling the modular addition of new optimized operations.
Related Classes/Methods: