Fix PyTorch Compatibility link and remove incomplete rows (#4195)

* fix pytorch-compatibility filename

fix links

* remove incomplete rows in pytorch-compatibility

* fix broken refs

docs/compatibility/compatibility-matrix-historical-6.0.csv

@@ -22,7 +22,7 @@ ROCm Version,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.
       ,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
       ,,,,,,,,,,,
       FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,
-      :doc:`PyTorch <../compatibility/pytorch-compatiblity>`,"2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
+      :doc:`PyTorch <../compatibility/pytorch-compatibility>`,"2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
       :doc:`TensorFlow <rocm-install-on-linux:install/3rd-party/tensorflow-install>`,"2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
       :doc:`JAX <rocm-install-on-linux:install/3rd-party/jax-install>`,0.4.35,0.4.35,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
       `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1

docs/compatibility/compatibility-matrix.rst

@@ -47,7 +47,7 @@ compatibility and system requirements.
       ,gfx908,gfx908,gfx908
       ,,,
       FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix:,,
-      :doc:`PyTorch <../compatibility/pytorch-compatiblity>`,"2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13"
+      :doc:`PyTorch <../compatibility/pytorch-compatibility>`,"2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13"
       :doc:`TensorFlow <rocm-install-on-linux:install/3rd-party/tensorflow-install>`,"2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1"
       :doc:`JAX <rocm-install-on-linux:install/3rd-party/jax-install>`,0.4.35,0.4.35,0.4.26
       `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.17.3,1.17.3,1.17.3

docs/compatibility/pytorch-compatibility.rst

@@ -576,14 +576,6 @@ PyTorch interacts with the CUDA or ROCm environment.
       - Globally enables or disables the PyTorch C++ implementation within SDPA.
       - 2.1
       - ❌
-    * - ``allow_fp16_bf16_reduction_math_sdp``
-      - Globally enables FP16 and BF16 precision for reduction operations within
-        SDPA.
-      - 2.1
-      - 
-..
-   FIXME:
-      - Partial?
 
 .. Need to validate and extend.
 
@@ -671,15 +663,6 @@ of computational resources and scalability for large-scale tasks.
         those on separate machines.
       - 1.8
       - 5.4
-    * - RPC Device Map Passing
-      - RPC Device Map Passing in PyTorch refers to a feature of the Remote
-        Procedure Call (RPC) framework that enables developers to control and
-        specify how tensors are transferred between devices during remote
-        operations. It allows fine-grained management of device placement when
-        sending tensors across nodes in distributed training or execution
-        scenarios.
-      - 1.9
-      - 
     * - Gloo
       - Gloo is designed for multi-machine and multi-GPU setups, enabling
         efficient communication and synchronization between processes. Gloo is
@@ -687,24 +670,6 @@ of computational resources and scalability for large-scale tasks.
         (DDP) and RPC frameworks, alongside other backends like NCCL and MPI.
       - 1.0
       - 2.0
-    * - MPI
-      - MPI (Message Passing Interface) in PyTorch refers to the use of the MPI
-        backend for distributed communication in the ``torch.distributed`` module.
-        It enables inter-process communication, primarily in distributed
-        training settings, using the widely adopted MPI standard.
-      - 1.9
-      -
-    * - TorchElastic
-      - TorchElastic is a PyTorch library that enables fault-tolerant and
-        elastic training in distributed environments. It is designed to handle
-        dynamically changing resources, such as adding or removing nodes during
-        training, which is especially useful in cloud-based or preemptible
-        environments.
-      - 1.9
-      -
-
-.. 
-   FIXME: RPC Device Map Passing "Since ROCm version"
 
 torch.compiler
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^