[PyTorch] Remove GH pinned deps (#1961)

* Remove GH pinned deps
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Pin onnxscript
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

---------
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

build_tools/pytorch.py

@@ -14,19 +14,7 @@ from typing import List
 
 def install_requirements() -> List[str]:
     """Install dependencies for TE/PyTorch extensions."""
-    reqs = ["torch>=2.1", "einops", "onnxscript"]
-    reqs.append(
-        "nvdlfw-inspect @"
-        " git+https://github.com/NVIDIA/nvidia-dlfw-inspect.git@v0.1#egg=nvdlfw-inspect"
-    )
-    reqs.extend(
-        [
-            "torch>=2.1",
-            "onnx",
-            "onnxscript@git+https://github.com/microsoft/onnxscript.git@51ecf47523ef079c53b0e620c62d56d70cfd3871",
-        ]
-    )
-    return reqs
+    return ["torch>=2.1", "einops", "onnxscript==0.3.1", "onnx"]
 
 
 def test_requirements() -> List[str]:

docs/debug/1_getting_started.rst

@@ -21,7 +21,7 @@ Transformer Engine provides a set of precision debug tools which allow you to ea
 There are 4 things one needs to do to use Transformer Engine debug features:
 
 1. Create a configuration YAML file to configure the desired features.
-2. Import, and initialize the `Nvidia-DL-Framework-Inspect <https://github.com/NVIDIA/nvidia-dlfw-inspect>`_ tool, which is installed as the dependency of the Transformer Engine.
+2. Import, initialize, and install the `Nvidia-DL-Framework-Inspect <https://github.com/NVIDIA/nvidia-dlfw-inspect>`_ tool.
 3. One can pass ``name="..."`` when creating TE layers to easier identify layer names. If this is not provided, names will be inferred automatically.
 4. Invoke ``debug_api.step()`` at the end of one forward-backward pass.