chore: fix docs

Signed-off-by: Dheeraj Peri <[email protected]>

chore: updates

Signed-off-by: Dheeraj Peri <[email protected]>

chore: remove dynamo_export file since it has been moved

Signed-off-by: Dheeraj Peri <[email protected]>

chore: updates

Signed-off-by: Dheeraj Peri <[email protected]>

chore: updates

Signed-off-by: Dheeraj Peri <[email protected]>

Author: peri044

docsrc/dynamo/dynamo_export.rst

@@ -1,6 +1,6 @@
 .. _dynamo_export:
 
-Compiling ``ExportedPrograms`` with Torch-TensorRT
+Compiling Exported Programs with Torch-TensorRT
 =============================================
 .. currentmodule:: torch_tensorrt.dynamo
 
@@ -9,8 +9,6 @@ Compiling ``ExportedPrograms`` with Torch-TensorRT
    :undoc-members:
    :show-inheritance:
 
-Using the Torch-TensorRT Frontend for ``torch.export.ExportedPrograms``
---------------------------------------------------------
 Pytorch 2.1 introduced ``torch.export`` APIs which
 can export graphs from Pytorch programs into ``ExportedProgram`` objects. Torch-TensorRT dynamo
 frontend compiles these ``ExportedProgram`` objects and optimizes them using TensorRT. Here's a simple
@@ -43,8 +41,7 @@ Some of the frequently used options are as follows:
 
 The complete list of options can be found `here <https://github.com/pytorch/TensorRT/blob/123a486d6644a5bbeeec33e2f32257349acc0b8f/py/torch_tensorrt/dynamo/compile.py#L51-L77>`_
 
-.. note:: We do not support INT precision currently in Dynamo. Support for this currently exists in
-our Torchscript IR. We plan to implement similar support for dynamo in our next release.
+.. note:: We do not support INT precision currently in Dynamo. Support for this currently exists in our Torchscript IR. We plan to implement similar support for dynamo in our next release.
 
 Under the hood
 --------------

docsrc/user_guide/dynamo_export.rst

@@ -29,14 +29,14 @@ The following code illustrates this approach.
     import torch_tensorrt
 
     model = MyModel().eval().cuda()
-    inputs = torch.randn((1, 3, 224, 224)).cuda()
+    inputs = [torch.randn((1, 3, 224, 224)).cuda()]
     trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) # Output is a torch.fx.GraphModule
-    trt_traced_model = torchtrt.dynamo.serialize(trt_gm, inputs)
+    trt_traced_model = torch.jit.trace(trt_gm, inputs)
     torch.jit.save(trt_traced_model, "trt_model.ts")
 
     # Later, you can load it and run inference
     model = torch.jit.load("trt_model.ts").cuda()
-    model(inputs)
+    model(*inputs)
 
 b) ExportedProgram
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -50,39 +50,40 @@ b) ExportedProgram
     import torch_tensorrt
 
     model = MyModel().eval().cuda()
-    inputs = torch.randn((1, 3, 224, 224)).cuda()
-    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs) # Output is a torch.fx.GraphModule
+    inputs = [torch.randn((1, 3, 224, 224)).cuda()]
+    exp_program = torch_tensorrt.dynamo.trace(model, inputs)
+    trt_gm = torch_tensorrt.dynamo.compile(exp_program, inputs) # Output is a torch.fx.GraphModule
     # Transform and create an exported program
-    trt_exp_program = torch_tensorrt.dynamo.export(trt_gm, inputs, call_spec, ir="exported_program")
+    trt_exp_program = torch_tensorrt.dynamo.export(trt_gm, inputs, exp_program.call_spec, ir="exported_program")
     torch.export.save(trt_exp_program, "trt_model.ep")
 
     # Later, you can load it and run inference 
     model = torch.export.load("trt_model.ep")
-    model(inputs)
+    model(*inputs)
 
 `torch_tensorrt.dynamo.export` inlines the submodules within a GraphModule to their corresponding nodes, stiches all the nodes together and creates an ExportedProgram. 
 This is needed as `torch.export` serialization cannot handle serializing and deserializing of submodules (`call_module` nodes). 
 
-NOTE: This way of saving the models using `ExportedProgram` is experimental. Here is a known issue : https://github.com/pytorch/TensorRT/issues/2341
+.. note:: This way of saving the models using `ExportedProgram` is experimental. Here is a known issue : https://github.com/pytorch/TensorRT/issues/2341
 
 
 Torchscript IR
 -------------
 
-  In Torch-TensorRT 1.X versions, the primary way to compile and run inference with Torch-TensorRT is using Torchscript IR.
-  This behavior stays the same in 2.X versions as well.
+In Torch-TensorRT 1.X versions, the primary way to compile and run inference with Torch-TensorRT is using Torchscript IR.
+This behavior stays the same in 2.X versions as well.
 
-  .. code-block:: python
+.. code-block:: python
 
-    import torch
-    import torch_tensorrt
+  import torch
+  import torch_tensorrt
 
-    model = MyModel().eval().cuda()
-    inputs = torch.randn((1, 3, 224, 224)).cuda()
-    trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object
-    torch.jit.save(trt_ts, "trt_model.ts")
+  model = MyModel().eval().cuda()
+  inputs = [torch.randn((1, 3, 224, 224)).cuda()]
+  trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object
+  torch.jit.save(trt_ts, "trt_model.ts")
 
-    # Later, you can load it and run inference
-    model = torch.jit.load("trt_model.ts").cuda()
-    model(inputs)
+  # Later, you can load it and run inference
+  model = torch.jit.load("trt_model.ts").cuda()
+  model(*inputs)
 

docsrc/user_guide/saving_models.rst

@@ -53,7 +53,7 @@ def export(
         return exp_program
     else:
         raise ValueError(
-            "Invalid ir : {ir} provided for serialization. Options include torchscript | exported_program"
+            f"Invalid ir : {ir} provided for serialization. Options include torchscript | exported_program"
         )