mirror of https://github.com/llvm/torch-mlir
138 lines
7.3 KiB
Markdown
138 lines
7.3 KiB
Markdown
# Torch-MLIR Lazy Tensor Core Backend
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## Table of Contents
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- [Introduction](#introduction)
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- [Examples](#examples)
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- [Code Structure](#code-structure)
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- [Architecture](#architecture)
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- [Implementing a custom backend](#implementing-a-custom-backend)
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- [Future Expansion](#future-expansion)
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## Introduction
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[Lazy Tensor Core](https://github.com/pytorch/pytorch/blob/master/torch/csrc/lazy/tutorial.md) is a tracing system in PyTorch which is supported as an entry point to Torch-MLIR.
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After registering an LTC backend, all operations performed on lazy tensors are recorded and handed off to the backend implementation.
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LTC support is provided through an abstract [`TorchMlirBackendImpl`](../python/torch_mlir/csrc/base_lazy_backend/backend_impl.h) class, which handles the conversion to MLIR.
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Implementations based on this abstract class will be able to specify their own compile and execution workflows.
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Additional details about how to implement a custom backend is available [below](#Implementing-a-custom-backend).
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## Examples
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View examples [here](ltc_examples.md).
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## Code Structure
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### Autogen Build Tools ([`build_tools`](../build_tools))
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- `autogen_ltc_backend.{py,yaml}`
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- The [autogen files](#autogen-files) are generated by this script based on the list of supported ops, which includes all ops from [`GeneratedTorchOps.td`](https://github.com/llvm/torch-mlir/blob/main/include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td),
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excluding those explicitly blacklisted in the YAML file
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### Autogen Files ([`python/torch_mlir/csrc/base_lazy_backend/generated`](../python/torch_mlir/csrc/base_lazy_backend/generated))
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Generated files are created in this directory, which is ignored by version control.
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- `LazyIr.h`
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- Definitions of `torch::lazy:TorchMlirNode` subclasses for each supported autogen op
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- `LazyNativeFunctions.{cpp,h}`
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- Native function definitions for each supported op (handles `at::Tensor -> at::Tensor` data flow and creation of `torch::lazy:TorchMlirNode`)
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- `LazyNonNativeIr.h`
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- Non-native `torch::lazy:TorchMlirNode` subclasses
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- `RegisterLazy.cpp`
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- Registers PyTorch kernels under the `lazy` dispatch key for all supported ops, which map to our native functions
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- `shape_inference.{cpp,h}`
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- Shape inference headers for supported ops and autogen'd placeholders for unimplemented functions
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### Base Backend ([`python/torch_mlir/csrc/base_lazy_backend`](../python/torch_mlir/csrc/base_lazy_backend))
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- `backend_impl.{cpp,h}`
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- Base LTC backend to setup Torch-MLIR lowering context
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- `dynamic_ir.{cpp,h}`
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- Manually implemented "dynamic" nodes
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- `ir_builder.h`
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- Torch-MLIR implementation of `torch::lazy::IrBuilder`
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- `mlir_lowering_context.h`
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- Handles conversion from `torch::lazy::Node` to MLIR via JIT and Torch-MLIR infrastructure
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- `mlir_native_functions.cpp`
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- Manually implemented native functions
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- `mlir_node.{cpp,h}`
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- Torch-MLIR implementation of `torch::lazy::Node`
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- `mlir_node_lowering.{cpp,h}`
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- Lower a `torch::lazy::Node` to JIT graph in preparation for MLIR generation
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- `shape_inference.cpp`
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- Implementation of select shape inference functions (most functions are [implemented upstream](https://github.com/pytorch/pytorch/blob/master/torch/csrc/lazy/core/shape_inference.cpp))
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### Reference Backend ([`python/torch_mlir/csrc/reference_lazy_backend`](../python/torch_mlir/csrc/reference_lazy_backend))
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- `backend_impl.{cpp,h}`
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- Reference Torch-MLIR LTC backend implementation, which simply stores the MLIR as a string and executes computation on CPU
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- `reference_lazy_backend_pybind.cpp`
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- pybind for reference Torch-MLIR LTC backend
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### Examples ([`examples`](../examples))
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- `ltc_backend_bert.py`
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- Example HuggingFace BERT model traced by LTC to MLIR
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- `ltc_backend_mnist.py`
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- Example MNIST model traced by LTC to MLIR
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## Architecture
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### Tracing LTC graph
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The journey begins with a tensor in PyTorch on the `lazy` device, which may undergo a number of operations during its lifetime.
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```python
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>>> lazy_backend._initialize()
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>>> x = torch.tensor(..., device='lazy')
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>>> y = torch.tanh(x)
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...
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```
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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`
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previously registered in `RegisterLazy.cpp`.
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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`.
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These nodes are then tracked internally by LTC as the computation graph is traced out.
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### Syncing Tensors
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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).
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```python
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>>> torch._lazy.mark_step()
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```
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This triggers a call to `LazyGraphExecutor::SyncLiveTensorsGraph` somewhere in the guts of LTC, which collects all the `TorchMlirNode`s (technically `torch::lazy::Node`s at this point) from the current trace and
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creates an instance of `TorchMlirLoweringContext`. Here, the `TorchMlirNode`s are lowered to JIT via `mlir_node_lowering.cpp` and inserted into a `jit::Graph`.
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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.
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At this point, a `TorchMlirComputation` is created containing the final `mlir::FuncOp`.
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### Final Compilation and Execution
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The `TorchMlirComputation` is sent to the vendor specific implementation of `TorchMlirBackendImpl::Compile` to be handed off to the vendor's compilation stack (if applicable).
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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.
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## Implementing a custom backend
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A reference implementation of a custom backend is available [here](../python/torch_mlir/csrc/reference_lazy_backend/).
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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.
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A pybind is needed to invoke C++ code to register the autogen PyTorch kernels and the custom backend itself.
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Most of the code in the reference implementation should be reusable, excluding some debug related function (e.g. `get_latest_computation`).
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## Future Expansion
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There are a number of areas for future improvement:
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- Generate source information in `jit::Graph` so it can be embedded in the MLIR
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- 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
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- In the future, we should change the implementation to lower the MLIR to linalg and execute on a reference backend
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- As new models get tested, we will inevitably run into errors related to unimplemented shape inference functions.
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This problem is simply solved by implementing the missing function, or adding a structured kernel to PyTorch.
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