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+# ZenDNN Integration
+
+
+## **Author**
+* @naveenthangudu
+
+
+## **Summary**
+This document proposes an approach for integrating ZenDNN library into PyTorch. This integration will enable inference optimizations for deep learning workloads on AMD CPUs.
+
+
+## **Highlights**
+* Default build behavior remains un-altered.
+* A pre-build step will generate ZenDNN integration code.
+* ZenDNN integration is optimized for inference workloads.
+* New opt-in build flag (environmental variable) `USE_ZENDNN` will be added.
+* No Submodules are added. When `USE_ZENDNN` is set to 1, the following repositories will be downloaded into third_party folder.
+ * [ZenDNN](https://github.com/amd/ZenDNN)
+ * [AOCL BLIS](https://github.com/amd/blis)
+* ZenDNN optimizations are added as JIT Passes on frozen graph under `optimize_for_inference` API.
+* Support will be added for fp32 and bf16 data types.
+
+
+## **Motivation**
+The main goal of this integration is to realize optimal inference on AMD CPUs for PyTorch. ZenDNN is a library which enables performance improvements on AMD CPU architectures. Please find the repo and more details [here](https://github.com/amd/ZenDNN). To highlight the potential uplift using ZenDNN vs. oneDNN on AMD CPUs, we have benchmarked our fully integrated ZenDNN version using PyTorch v1.12 on a Genoa CPU (4th Generation AMD EPYC™ Processors).
+
+
+### **Benchmarking Configuration**
+|||
+|-----------------------------|-------|
+| PyTorch version | v1.12 |
+| CPU | 4th Generation AMD EPYC™ Genoa CPU |
+| Number of physical cores | 96 |
+| NUMA Nodes per Socket(NPS) | 1 |
+| Model data type | FP32 |
+
+### **Latency Performance - batch_size=1**
+
+
+
+### **Throughput Performance - batch_size=640**
+
+
+
+
+## **Proposed Implementation**
+
+### **Overview**
+This approach references the [hipify](https://github.com/ROCm-Developer-Tools/HIPIFY) approach used by the AMD ROCm team for integration of ROCm libraries into PyTorch. By leveraging shared APIs between oneDNN and ZenDNN, a pre-build step will generate ZenDNN integration code wherever possible. By adding an opt-in build flag, default build behavior is un-altered. Required repositories are only downloaded when the build flag is enabled.
+
+### **Code generation script**
+* References hipify tool, which is present in PyTorch.
+* Implemented in Python and run as a pre-build step.
+* Takes in folders or files and generates substituted code.
+
+### **Build Infrastructure**
+* Support for opt-in flag (environmental variable) `USE_ZENDNN` in Linux build path.
+* PyTorch build reverts to default behavior if `USE_ZENDNN` environmental
+variable is set to zero or unset.
+* When `USE_ZENDNN` is set to 1,
+ * ZenDNN and AOCL BLIS repositories will be downloaded into third_party folder.
+ * AOCL BLIS and ZenDNN will be built into PyTorch.
+ * oneDNN path will be disabled.
+* BLIS library is built as a pure dependency for ZenDNN acceleration. MKL will continue to be the BLAS library used by default.
+
+
+### **Graph Optimizations**
+As part of this integration, the following optimizations will be added into PyTorch. As optimization is an ongoing process, we will be adding more optimizations in the future.
+* Fusion of ops. Examples include,
+ * Conv Bias and Relu (CBR) fusions
+ * Conv and Add fusions (Fusing Residual addition into conv)
+ * Inplace concatenation (Inception)
+* Reorder optimizations
+* Inplace unary elementwise operations
+* Memory Pool optimizations
+
+As a few optimizations are specific to inference only workloads, our graph optimizations are added onto frozen graphs. They are added under `optmize_for_inference` API. The figure below illustrates ZenDNN graph optimizations in the PyTorch workflow.
+
+
+
+## **Open Questions**
+The following questions will impact the design and implementation of ZenDNN integration.
+
+* Can ZenDNN have a new dispatch key added specifically for it?
+* Can ZenDNN have its own backend?
+* Can we add ZenDNN tensor layout to tensor layouts?
+
+
+## **Next Steps**
+* First PR - with code generation tool and build infrastructural changes with support for eager mode.
+* Second PR - featuring a few graph optimizations.
+* Third PR - including unit tests and CI deployment.
+* Further PRs with more optimizations and PyTorch 2.0 feature integration.
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+
+### **Throughput Performance - batch_size=640**
+
+
+
+
+## **Proposed Implementation**
+
+### **Overview**
+This approach references the [hipify](https://github.com/ROCm-Developer-Tools/HIPIFY) approach used by the AMD ROCm team for integration of ROCm libraries into PyTorch. By leveraging shared APIs between oneDNN and ZenDNN, a pre-build step will generate ZenDNN integration code wherever possible. By adding an opt-in build flag, default build behavior is un-altered. Required repositories are only downloaded when the build flag is enabled.
+
+### **Code generation script**
+* References hipify tool, which is present in PyTorch.
+* Implemented in Python and run as a pre-build step.
+* Takes in folders or files and generates substituted code.
+
+### **Build Infrastructure**
+* Support for opt-in flag (environmental variable) `USE_ZENDNN` in Linux build path.
+* PyTorch build reverts to default behavior if `USE_ZENDNN` environmental
+variable is set to zero or unset.
+* When `USE_ZENDNN` is set to 1,
+ * ZenDNN and AOCL BLIS repositories will be downloaded into third_party folder.
+ * AOCL BLIS and ZenDNN will be built into PyTorch.
+ * oneDNN path will be disabled.
+* BLIS library is built as a pure dependency for ZenDNN acceleration. MKL will continue to be the BLAS library used by default.
+
+
+### **Graph Optimizations**
+As part of this integration, the following optimizations will be added into PyTorch. As optimization is an ongoing process, we will be adding more optimizations in the future.
+* Fusion of ops. Examples include,
+ * Conv Bias and Relu (CBR) fusions
+ * Conv and Add fusions (Fusing Residual addition into conv)
+ * Inplace concatenation (Inception)
+* Reorder optimizations
+* Inplace unary elementwise operations
+* Memory Pool optimizations
+
+As a few optimizations are specific to inference only workloads, our graph optimizations are added onto frozen graphs. They are added under `optmize_for_inference` API. The figure below illustrates ZenDNN graph optimizations in the PyTorch workflow.
+
+
+
+## **Open Questions**
+The following questions will impact the design and implementation of ZenDNN integration.
+
+* Can ZenDNN have a new dispatch key added specifically for it?
+* Can ZenDNN have its own backend?
+* Can we add ZenDNN tensor layout to tensor layouts?
+
+
+## **Next Steps**
+* First PR - with code generation tool and build infrastructural changes with support for eager mode.
+* Second PR - featuring a few graph optimizations.
+* Third PR - including unit tests and CI deployment.
+* Further PRs with more optimizations and PyTorch 2.0 feature integration.
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