[docs] Centralize all images in docs/images/

llvm · Nov 4, 2022 · de4bcbf · de4bcbf
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Showing 10 changed files with 6 additions and 6 deletions.
diff --git a/README.md b/README.md
@@ -22,7 +22,7 @@ Multiple Vendors use MLIR as the middle layer, mapping from platform frameworks
 
 We have few paths to lower down to the Torch MLIR Dialect.
 
-![Torch Lowering Architectures](docs/Torch-MLIR.png)
+![Simplified Architecture Diagram for README](docs/images/readme_architecture_diagram.png)
 
  - TorchScript
     This is the most tested path down to Torch MLIR Dialect, and the PyTorch ecosystem is converging on using TorchScript IR as a lingua franca.

diff --git a/docs/architecture.md b/docs/architecture.md
@@ -15,7 +15,7 @@ halves interface at an abstraction layer that we call the "backend contract",
 which is a subset of the `torch` dialect with certain properties appealing for
 backends to lower from.
 
-![Torch-MLIR Architecture](Torch-MLIR_Architecture.png)
+![Torch-MLIR Architecture](images/architecture.png)
 
 The frontend of Torch-MLIR is concerned with interfacing to PyTorch itself, and
 then normalizing the program to the "backend contract". This part involves build

diff --git a/docs/Torch-MLIR_Architecture.png → docs/images/architecture.png b/docs/Torch-MLIR_Architecture.png → docs/images/architecture.png
diff --git a/docs/ltc_images/ltc_architecture.png → docs/images/ltc_architecture.png b/docs/ltc_images/ltc_architecture.png → docs/images/ltc_architecture.png
diff --git a/docs/ltc_images/syncing_tensors.png → docs/images/ltc_syncing_tensors.png b/docs/ltc_images/syncing_tensors.png → docs/images/ltc_syncing_tensors.png
diff --git a/docs/ltc_images/tracing_tensors.png → docs/images/ltc_tracing_tensors.png b/docs/ltc_images/tracing_tensors.png → docs/images/ltc_tracing_tensors.png
diff --git a/docs/ltc_images/vendor_execution.png → docs/images/ltc_vendor_execution.png b/docs/ltc_images/vendor_execution.png → docs/images/ltc_vendor_execution.png
diff --git a/docs/Torch-MLIR.excalidraw → ...es/readme_architecture_diagram.excalidraw b/docs/Torch-MLIR.excalidraw → ...es/readme_architecture_diagram.excalidraw
diff --git a/docs/Torch-MLIR.png → docs/images/readme_architecture_diagram.png b/docs/Torch-MLIR.png → docs/images/readme_architecture_diagram.png
diff --git a/docs/ltc_backend.md b/docs/ltc_backend.md
@@ -76,7 +76,7 @@ Generated files are created in this directory, which is ignored by version contr
 
 ## Architecture
 
-![LTC Diagram](ltc_images/ltc_architecture.png)
+![LTC Diagram](images/ltc_architecture.png)
 
 ### Tracing LTC graph
 
@@ -93,7 +93,7 @@ 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.
 
-![Tracing Tensors](ltc_images/tracing_tensors.png)
+![Tracing Tensors](images/ltc_tracing_tensors.png)
 
 ### Syncing Tensors
 
@@ -109,15 +109,15 @@ creates an instance of `TorchMlirLoweringContext`. Here, the `TorchMlirNode`s ar
 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`.
 
-![Syncing Tensors](ltc_images/syncing_tensors.png)
+![Syncing Tensors](images/ltc_syncing_tensors.png)
 
 ### 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.
 
-![Vendor Execution](ltc_images/vendor_execution.png)
+![Vendor Execution](images/ltc_vendor_execution.png)
 
 ## Implementing a custom backend