ltc_backend.md

Torch-MLIR Lazy Tensor Core Backend

Table of Contents

• Introduction
• Examples
• Code Structure
• Architecture
• Implementing a custom backend
• Future Expansion

Introduction

Lazy Tensor Core is a tracing system in PyTorch which is supported as an entry point to Torch-MLIR. After registering an LTC backend, all operations performed on lazy tensors are recorded and handed off to the backend implementation.

LTC support is provided through an abstract TorchMlirBackendImpl class, which handles the conversion to MLIR. Implementations based on this abstract class will be able to specify their own compile and execution workflows. Additional details about how to implement a custom backend is available below.

Examples

View examples here.

Code Structure

Autogen Build Tools (build_tools)

• autogen_ltc_backend.{py,yaml}
  • The autogen files are generated by this script based on the list of supported ops, which includes all ops from GeneratedTorchOps.td, excluding those explicitly blacklisted in the YAML file

Autogen Files (projects/ltc/csrc/base_lazy_backend/generated)

Generated files are created in this directory, which is ignored by version control.

• LazyIr.h
  • Definitions of torch::lazy:TorchMlirNode subclasses for each supported autogen op
• LazyNativeFunctions.{cpp,h}
  • Native function definitions for each supported op (handles at::Tensor -> at::Tensor data flow and creation of torch::lazy:TorchMlirNode)
• LazyNonNativeIr.h
  • Non-native torch::lazy:TorchMlirNode subclasses
• RegisterLazy.cpp
  • Registers PyTorch kernels under the lazy dispatch key for all supported ops, which map to our native functions
• shape_inference.{cpp,h}
  • Shape inference headers for supported ops and autogen'd placeholders for unimplemented functions

Base Backend (projects/ltc/csrc/base_lazy_backend)

• backend_impl.{cpp,h}
  • Base LTC backend to setup Torch-MLIR lowering context
• dynamic_ir.{cpp,h}
  • Manually implemented "dynamic" nodes
• ir_builder.h
  • Torch-MLIR implementation of torch::lazy::IrBuilder
• mlir_lowering_context.h
  • Handles conversion from torch::lazy::Node to MLIR via JIT and Torch-MLIR infrastructure
• mlir_native_functions.cpp
  • Manually implemented native functions
• mlir_node.{cpp,h}
  • Torch-MLIR implementation of torch::lazy::Node
• mlir_node_lowering.{cpp,h}
  • Lower a torch::lazy::Node to JIT graph in preparation for MLIR generation
• shape_inference.cpp
  • Implementation of select shape inference functions (most functions are implemented upstream)

Reference Backend (python/torch_mlir/csrc/reference_lazy_backend)

• backend_impl.{cpp,h}
  • Reference Torch-MLIR LTC backend implementation, which simply stores the MLIR as a string and executes computation on CPU
• reference_lazy_backend_pybind.cpp
  • pybind for reference Torch-MLIR LTC backend

Examples (examples)

• ltc_backend_bert.py
  • Example HuggingFace BERT model traced by LTC to MLIR
• ltc_backend_mnist.py
  • Example MNIST model traced by LTC to MLIR

Architecture

Tracing LTC graph

The journey begins with a tensor in PyTorch on the lazy device, which may undergo a number of operations during its lifetime.

>>> lazy_backend._initialize()
>>> x = torch.tensor(..., device='lazy')
>>> y = torch.tanh(x)
...

The call to torch.tanh triggers a chain of events. PyTorch checks the dispatch table under the lazy key and finds the kernel for tanh previously registered in RegisterLazy.cpp.

Next, LazyNativeFunctions::tanh from LazyNativeFunctions.cpp is called, which triggers the creation of a Tanh node, which is a subclass of TorchMlirNode and torch::lazy::Node, defined in LazyIr.h. These nodes are then tracked internally by LTC as the computation graph is traced out.

Syncing Tensors

At some point, the tensors will be synced in order to execute the computation -- either explicitly via mark_step, or implicitly through some operation that requires the contents of the tensors (e.g. printing to console).

>>> torch._lazy.mark_step()

This triggers a call to LazyGraphExecutor::SyncLiveTensorsGraph somewhere in the guts of LTC, which collects all the TorchMlirNodes (technically torch::lazy::Nodes at this point) from the current trace and creates an instance of TorchMlirLoweringContext. Here, the TorchMlirNodes are lowered to JIT via mlir_node_lowering.cpp and inserted into a jit::Graph.

Next, TorchMlirLoweringContext::Build is executed and the final jit::Graph is sent to torch_mlir::importJitFunctionAsFuncOp to generate MLIR using the existing infrastructure from Torch-MLIR. At this point, a TorchMlirComputation is created containing the final mlir::FuncOp.

Final Compilation and Execution

The TorchMlirComputation is sent to the vendor specific implementation of TorchMlirBackendImpl::Compile to be handed off to the vendor's compilation stack (if applicable).

Finally, the compiled computation is sent to TorchMlirBackendImpl::ExecuteComputation to be executed on the vendor device, which produces some results to be send back to PyTorch.

Implementing a custom backend

A reference implementation of a custom backend is available here. All the work involved with generating MLIR is handled in the base LTC backend, so vendors only need to worry about implementing Compile, ExecuteComputation, and some other minor methods to interface with the device.

A pybind is needed to invoke C++ code to register the autogen PyTorch kernels and the custom backend itself. Most of the code in the reference implementation should be reusable, excluding some debug related function (e.g. get_latest_computation).

Future Expansion

There are a number of areas for future improvement:

• Generate source information in jit::Graph so it can be embedded in the MLIR
• Currently the reference backend implementation executes via the jit::Graph instead of the MLIR since we currently lack lowerings for many ops, which would make it difficult to run models such as HF BERT
  • In the future, we should change the implementation to lower the MLIR to linalg and execute on a reference backend
• As new models get tested, we will inevitably run into errors related to unimplemented shape inference functions. This problem is simply solved by implementing the missing function, or adding a structured kernel to PyTorch.