Addressing review comment and removing FX portion

Author: apbose

docsrc/contributors/fx_converters.rst

@@ -1,36 +1,27 @@
-.. _conversion:
+.. _dynamo_conversion:
 
-FX Converters
+Dynamo Converters
 ==================
-The FX converter library in Torch-TensorRT is located in ``TensorRT/py/torch_tensorrt/fx/converters`` (Converters present in FX will soon be deprecated) and ``TensorRT/py/torch_tensorrt/dynamo/conversion``.
-FX converters are categorized into - ``aten_ops_converters``, ``acc_ops_converters`` and ``nn_ops_converters``, while dynamo converters only cover ``aten_ops_converters``
-The individual converters present in the above folders are useful for the quantization workflow.
+The dynamo converter library in Torch-TensorRT is located in ``TensorRT/py/torch_tensorrt/dynamo/conversion``.
 
-The dynamo converters are registered using the ``dynamo_tensorrt_converter`` and the FX converters are registered using the ``tensorrt_converter``.
-Since FX converters will be deprecated soon, this document will focus more on the dynamo converters.
 
 
 Steps
 ==================
 
-Operation Sets
+Operation Set
 -------------------
-There are three different converter sets for FX in torch_tensorrt. Depending on whether the operation is generated using acc_trace, aten_trace or fx_trace, the converters are categorized to one of the three operation sets - 
-``aten_ops_converters``, ``acc_ops_converters`` or ``nn_ops_converters``.  The converters are registered using ``tensorrt_converter`` decorator for FX and ``dynamo_tensorrt_converter`` for dynamo. The function decorated
+The converters in dynamo are produced by ``aten_trace`` and falls under ``aten_ops_converters`` ( FX earlier had ``acc_ops_converters``, ``aten_ops_converters`` or ``nn_ops_converters`` depending on the trace through which it was produced).  The converters are registered using  ``dynamo_tensorrt_converter`` for dynamo. The function decorated
 has the arguments - ``network, target, args, kwargs, name``,  which is common across all the operators schema.
 These functions are mapped in the ``aten`` converter registry dictionary (at present a compilation of FX and dynamo converters, FX will be deprecated soon), with key as the function target name.
 
-    * acc_ops_converters
-        *  acc_trace is produced by ``torch_tensorrt.fx.tracer.acc_tracer.acc_tracer.trace``.
-    * aten_ops
-        There are two options at present for this
-            #.   Dynamo: aten_trace is produced by ``torch_tensorrt.dynamo.backend.compile``. The second round of trace is produced by ``aot_torch_tensorrt_aten_backend`` by invoking ``aot_module_simplified`` from ``torch._functorch.aot_autograd``
-            #.   FX: aten_trace is produced by ``torch_tensorrt.fx.tracer.dispatch_tracer.aten_tracer.trace``. This flow is more common currently, but this will soon be deprecated in torch_tensorrt.
-    * nn_ops
-        *  symbolic_trace is produced by ``torch.fx._symbolic_trace``.
+    * aten_trace is produced by ``torch_tensorrt.dynamo.backend.compile`` or  ``torch_tensorrt.dynamo.backend.export``.
+    The second round of trace in compile  produced by ``aot_torch_tensorrt_aten_backend`` by invoking ``aot_module_simplified`` from ``torch._functorch.aot_autograd``,
+    Both these simplify the torch operators to reduced set of Aten operations.
+        
 
 As mentioned above, if you would like to add a new converter, its implementation will be included in ``TensorRT/py/torch_tensorrt/dynamo/conversion/impl``
-Although there is a corresponding implementation of the converters  included in the common implementation library present in ``TensorRT/py/torch_tensorrt/fx/impl`` for FX converters, this documentation focuses on the implementation of the ``aten_ops`` converters in dynamo. There might be some steps involved in reorganizing files for ``acc_ops`` converters. This is discussed in more detail in the next section.
+Although there is a corresponding implementation of the converters  included in the common implementation library present in ``TensorRT/py/torch_tensorrt/fx/impl`` for FX converters, this documentation focuses on the implementation of the ``aten_ops`` converters in dynamo.
 
 
 Converter implementation
@@ -40,60 +31,14 @@ Each of them is detailed with the help of an example
 
     * Registration
 
-        The converter needs to be registered with the appropriate op code in the ``tensorrt_converter`` and ``dynamo_tensorrt_converter``. 
+        The converter needs to be registered with the appropriate op code in the ``dynamo_tensorrt_converter``. 
 
         * Activation type
 
             Example: ``leaky_relu``
 
-            * acc_ops_converters
-
-                * FX_converters (soon to be deprecated)
-                
-                    Define in ``py/torch_tensorrt/fx/converters/acc_ops_converters``. One needs to register the opcode generated in the trace, with ``tensorrt_converter`` decorator. Op code to be used for the registration or the converter registry key in this case is ``acc_ops.leaky_relu``
-
-                    .. code-block:: python
-
-                        @tensorrt_converter(acc_ops.leaky_relu)
-                        def acc_ops_leaky_relu(
-                            network: TRTNetwork,
-                            target: Target,
-                            args: Tuple[Argument, ...],
-                            kwargs: Dict[str, Argument],
-                            name: str,
-                        ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-                                input_val = kwargs["input"]
-                                negative_slope = kwargs["negative_slope"]
-                                operation_type = trt.ActivationType.LEAKY_RELU
-                                return activation.leaky_relu(
-                                    network, target, SourceIR.ACC, name, kwargs["input"], kwargs["negative_slope"]
-                                )
-
-                    Note since the above is deprecated, you may need to revisit these files only for file reorganization.
-
-                * Dynamo_converters
-
-                    The ``acc_ops`` are not present in dynamo converters
 
-            * aten_ops_converters
-
-                * FX_converters (soon to be deprecated)
-
-                    Define in ``py/torch_tensorrt/fx/converters/aten_ops_converters``. One needs to register the opcode generated in the trace with ``tensorrt_converter`` decorator. Op code to be used for the registration or the converter registry key in this case is ``torch.ops.aten.leaky_relu.default``
-
-                        .. code-block:: python
-            
-                            @tensorrt_converter(torch.ops.aten.leaky_relu.default)
-                                def aten_ops_leaky_relu(
-                                    network: TRTNetwork,
-                                    target: Target,
-                                    args: Tuple[Argument, ...],
-                                    kwargs: Dict[str, Argument],
-                                    name: str,
-                                ) -> Union[TRTTensor, Sequence[TRTTensor]]:
-                                        return activation.leaky_relu(network, target, SourceIR.ATEN, name, args[0], args[1])
-
-                * Dynamo_converters
+            * aten_ops_converters: Dynamo_converters
 
                     Define in ``py/torch_tensorrt/dynamo/conversion/aten_ops_converters``. One needs to register the opcode generated in the trace with ``dynamo_tensorrt_converter`` decorator. Op code to be used for the registration or the converter registry key in this case is ``torch.ops.aten.leaky_relu.default``
 
@@ -109,7 +54,7 @@ Each of them is detailed with the help of an example
                                 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
                                         return activation.leaky_relu(network, target, SourceIR.ATEN, name, args[0], args[1])
 
-            The ``tensorrt_converter`` and ``dynamo_tensorrt_converter`` are similar decorator functions with some differences. 
+            The ``tensorrt_converter`` (used for FX registration) and ``dynamo_tensorrt_converter`` are similar decorator functions with some differences. 
 
             #. Both register the converters in the registeries (python dictionaries) - ``CONVERTERS`` and ``DYNAMO_CONVERTERS`` respectively. These are two dictioneries which are concatenated to form the overall converter registry 
             #. The dictionary is keyed on the ``OpOverLoad`` which is mentioned in more detail below with examples 
@@ -136,21 +81,19 @@ Each of them is detailed with the help of an example
             The function decorated by ``tensorrt_converter`` and ``dynamo_tensorrt_converter`` has the following arguments which are automatically generated by the trace functions mentioned above.
 
             #. network : Node in the form of ``call_module`` or ``call_function`` having the target as the key
-            #. target: Target key in the ``call_module`` or ``call_function`` above. eg: ``torch.ops.aten_.leaky_relu.default``. Note that ``torch.ops.aten._leaky_relu`` is the ``OpOverloadPacket`` while ``torch.ops.aten_.leaky_relu.default`` is ``OpOverload``. The 
+            #. target: Target key in the ``call_module`` or ``call_function`` above. eg: ``torch.ops.aten_.leaky_relu.default``. Note that ``torch.ops.aten._leaky_relu`` is the ``OpOverloadPacket`` while ``torch.ops.aten_.leaky_relu.default`` is ``OpOverload``.
             #. args: The arguments passed in the ``call_module`` or ``call_function`` above
             #. kwargs: The kwargs passed in the ``call_module`` or ``call_function`` above
             #. name: String containing the name of the target
 
-            As a user writing new converters, one just needs to take care that the approriate arguments are extracted from the trace generated to the implementation function in the implementation lib function ``activation.leaky_relu`` (which we will discuss below in detail). As one can see in the example above, the trace for ``acc_op`` and ``aten_op`` is different.
-            ``Acc_ops`` has arguments in the ``args`` whereas ``aten_ops`` has arguments in the ``kwargs`` in the trace.
-
+            As a user writing new converters, one just needs to take care that the approriate arguments are extracted from the trace generated to the implementation function in the implementation lib function ``activation.leaky_relu`` (which we will discuss below in detail).
 
         * Operation type
 
             Example: ``fmod``
 
             It follows the same steps as the above converter. In this case the opcode is ``torch.ops.aten.fmod.Scalar`` or ``torch.ops.aten.fmod.Tensor``. 
-            Hence both the opcodes are registered in ``py/torch_tensorrt/fx/converters/aten_ops_converters`` and ``py/torch_tensorrt/dynamo/conversion/aten_ops_converters``.  The opcode is ``acc_ops.fmod`` in ``py/torch_tensorrt/fx/converters/acc_ops_converters``.
+            Hence both the opcodes are registered in ``py/torch_tensorrt/dynamo/conversion/aten_ops_converters``.
             Note that ``torch.ops.aten.fmod`` is the ``OpOverLoadPacket`` while the registry is keyed on ``torch.ops.aten.fmod.Scalar`` or ``torch.ops.aten.fmod.Tensor``, which is ``OpOverLoad``
 
             Example: ``embedding``
@@ -165,7 +108,7 @@ Each of them is detailed with the help of an example
                         torch.ops.aten.embedding.default, capability_validator=embedding_param_validator
                     )
 
-            So if there is a new converted in which certain special cases are not to be supported then they can be specified in the ``capability_validator``.
+            So if there is a new converter in which certain special cases are not to be supported then they can be specified in the ``capability_validator``.
 
         * Evaluator type
 
@@ -177,14 +120,13 @@ Each of them is detailed with the help of an example
 
     * Implementation Library
 
-        The converters across all the above three opsets have the common implementation library. FX converters would be ``py/torch_tensorrt/fx/converters/impl`` and dynamo converters would be ``py/torch_tensorrt/dynamo/conversion/impl``
-        Again as mentioned above, one should focus on the dynamo converters which are implemented in ``py/torch_tensorrt/dynamo/conversion/impl``
+        The dynamo converters would be located in ``py/torch_tensorrt/dynamo/conversion/impl``
 
         * Activation
 
             Example: ``leaky_relu``
 
-            The implementation is to be placed in present in ``py/torch_tensorrt/fx/impl/activation.py``. This is where all the activation functions are defined and implemented.
+            The implementation is to be placed in present in ``py/torch_tensorrt/dynamo/conversion/impl/activation.py``. This is where all the activation functions are defined and implemented.
 
             .. code-block:: python
 
@@ -202,22 +144,22 @@ Each of them is detailed with the help of an example
 
             #. network : ``network`` passed from the decorated function registration
             #. target: ``target`` passed from the decorated function registration
-            #. source_ir: Enum attribute. ``SourceIR`` enum is defined in ``py/torch_tensorrt/fx/converters/impl/converter_utils``
+            #. source_ir: Enum attribute. ``SourceIR`` enum is defined in ``py/torch_tensorrt/dynamo/conversion/impl/converter_utils``
             #. name: ``name`` passed from the decorated function registration
             #. input_val: Approriate arguments extracted from the decorated function registration from args or kwargs
             #. alpha: Approriate arguments extracted from the decorated function registration from args or kwargs. If not None, it will set the alpha attribute of the created TensorRT activation layer eg: Used in leaky_relu, elu, hardtanh           
             #. beta: Approriate arguments extracted from the decorated function registration from args or kwargs. If not None, it will set the beta attribute of the created TensorRT activation layer eg: Used in hardtanh
             #. dyn_range_fn: A optional function which takes the dynamic range of a TensorRT Tensor and returns the output dynamic range
 
-            The implementation functions call the ``convert_activation`` function in ``py/torch_tensorrt/fx/impl/activation.py``. This function will add the approriate activation layer via ``network.add_activation``.
+            The implementation functions call the ``convert_activation`` function in ``py/torch_tensorrt/dynamo/conversion/impl/activation.py``. This function will add the approriate activation layer via ``network.add_activation``.
 
         * Operator
 
-            The implementation is to be placed in ``py/torch_tensorrt/fx/impl/elementwise/ops.py`` for FX and ``py/torch_tensorrt/dynamo/conversion/impl`` for dynamo. This is where all the elementwise functions are defined and implemented.
+            The implementation is to be placed in ``py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py`` for dynamo. This is where all the elementwise functions are defined and implemented.
             For a new operator, one should identify the category to which it belongs. Following are some examples
 
-            #. Elementwise operators like ``fmod`` is present in ``py/torch_tensorrt/dynamo/conversion/impl/elementwise``. The ``py/torch_tensorrt/fx/impl/elementwise/base`` contains base functions for elementwise operator.
-            #. Unary operators like ``sqrt`` will be present in ``py/torch_tensorrt/dynamo/conversion/impl/unary``. The ``py/torch_tensorrt/fx/impl/unary/base`` contains base functions for unary operator.
+            #. Elementwise operators like ``fmod`` is present in ``py/torch_tensorrt/dynamo/conversion/impl/elementwise``. The ``py/torch_tensorrt/dynamo/conversion/impl/elementwise/base`` contains base functions for elementwise operator.
+            #. Unary operators like ``sqrt`` will be present in ``py/torch_tensorrt/dynamo/conversion/impl/unary``. The ``py/torch_tensorrt/dynamo/conversion/impl/unary/base`` contains base functions for unary operator.
             #. Normalization operators like ``softmax``, ``layer_norm``, ``batch_norm`` will be present in ``py/torch_tensorrt/dynamo/conversion/impl/normalization``. Since there are no base operations common to all, there is no base file. But one can choose to implement a base file, if there are common functions across all normalization operations
             #. Individual operators like ``slice``, ``select``, ``where``, ``embedding`` will be present in ``py/torch_tensorrt/dynamo/conversion/impl/*.py``. They will have individual operator implementation with the same API structure as above but with different individual arguments
 
@@ -244,21 +186,17 @@ Each of them is detailed with the help of an example
                 return torch.add(
                     torch.mul(input_, beta), torch.mul(torch.matmul(mat1, mat2), alpha)
                 )
+        
+        Note that there are some pre-existing dynamo decompositions in torch directory, in which case they should be used,
+        In that case please enable the decompositions in ``py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py`` in ``torch_enabled_decompositions``.
+        Similarly you can choose to disable any in ``torch_disabled_decompositions``. Please note that the ones already defined in the lowering will take precedence over torch lowering ops.
+
 
 
 
 Tests
 -----
 
-* FX testing: 
-    
-    Implement the fx tests in ``py/torch_tensorrt/fx/test/converters/aten_op/test_<operator_name>_aten.py``. Derive the test class from ``DispatchTestCase``, with parameterized testing to implement different test cases. Check for the following two conditions
-    
-    #. Compare the results for ``dispatch_tracer.aten_trace`` and torch.
-    #. Test the ``expected_op``. You can find examples in the above tests. This op will be called by the model and needs to be specified in the test so that the test checks that the approriate converter is invoked
-        
-The tests should fail if any of the above two conditions fail
-
 * Dynamo testing: 
 
     Dynamo tests are present for the lowering ops in ``py/torch_tensorrt/dynamo/backend/test/test_decompositions.py``. The above converters will soon be ported to dynamo tests