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55 lines
2.8 KiB
Markdown
55 lines
2.8 KiB
Markdown
# hipDNN Operation Support
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This document provides an overview of operation support in hipDNN and guides you to detailed information about specific plugin implementations.
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## About hipDNN Operation Support
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hipDNN is a plugin-based deep learning library that provides graph-based operation support through various backend plugins. Each plugin implements specific operations with support for different datatypes, layouts, and features.
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> [!IMPORTANT]
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> ⚠️ **hipDNN is in the early phase of development.** Operation support continues to expand as the library matures. Check individual plugin documentation for current support details.
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## Plugin-Specific Operation Support
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hipDNN operations are implemented through plugins. Each plugin provides its own set of supported operations. For detailed information about what operations are available, please refer to the plugin-specific documentation:
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### Available Plugins
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- **[MIOpen Provider Plugin](../../../dnn-providers/miopen-provider/docs/OperationSupport.md)** - Integration with AMD's MIOpen library for GPU-accelerated deep learning operations
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- Convolution operations (Forward, Dgrad, Wgrad)
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- Batchnorm operations (Training, Backward, Inference)
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- Fused operation graphs
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- **[hipBLASLt Provider Plugin](../../../dnn-providers/hipblaslt-provider/docs/OperationSupport.md)** - Integration with AMD's hipBLASLt library that provides optimized GEMM operations.
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- Stand-alone Matmul (FP32/FP16/BF16 inputs and outputs, with optional fused bias and activation)
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- OCP FP8 BlockScaleDequantize + GEMM (MX GEMM with VEC32_UE8M0 block scales; gfx950/gfx1250 only)
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### Reference Implementation
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- **[CPU Reference Implementation](./OperationSupport-ReferenceImpl.md)** - CPU-based reference implementation for validation and testing
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- Provides ground-truth results for validating GPU implementations
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- Supports core operations (Convolution, Batchnorm, Pointwise)
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- Not intended for performance or production use
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## Roadmap
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For information about planned features, upcoming operations, and the development roadmap, please see:
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- **[hipDNN Roadmap](./Roadmap.md)** - Comprehensive roadmap covering all hipDNN components, including planned plugin enhancements and new operation support
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## Contributing
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We welcome contributions to expand operation support in hipDNN!
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For detailed contribution guidelines, please see:
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- **[Contributing Guide](../CONTRIBUTING.md)** - Complete guide to contributing to hipDNN
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- **[Plugin Development](./PluginDevelopment.md)** - Guide for creating and extending plugins
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### Getting Started
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1. Review the [hipDNN Design](./Design.md) to understand the architecture
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2. Check the [Roadmap](./Roadmap.md) for planned features and contribution opportunities
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3. Open a GitHub issue to discuss your planned contribution
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4. Follow the [Contributing Guide](../CONTRIBUTING.md) for code quality and testing requirements
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