fix: Reorganize Dynamo backends

- Rename key backends to establish default backend and optional
alternatives
- Update function headers and docstrings, as well as key imports
- Rename `torch_compile` folder to `backends` in accordance with
`torch.compile` terminology
- Update references throughout codebase
- Specify certain functions as private/helper via underscore

Author: gs-olive

.circleci/config.yml

@@ -763,33 +763,33 @@ commands:
       - store_artifacts:
           path: /tmp/testlogs
 
-  test-dynamo-torch_compile-core:
-    description: "Test the Dynamo torch_compile path"
+  test-dynamo-compile-core:
+    description: "Test the Dynamo compile path"
     steps:
       - run:
-          name: Run Dynamo torch_compile core tests
+          name: Run Dynamo compile core tests
           command: |
-            cd py/torch_tensorrt/dynamo/torch_compile
+            cd py/torch_tensorrt/dynamo/backend
             pushd test/
-            pytest --junitxml=/tmp/artifacts/test_results/dynamo/torch_compile/test_results.xml
+            pytest --junitxml=/tmp/artifacts/test_results/dynamo/backend/test_results.xml
             popd
 
       - store_test_results:
           path: /tmp/artifacts
       - store_artifacts:
           path: /tmp/testlogs
 
-  test-dynamo-torch_compile:
-    description: "Test the Dynamo torch_compile path"
+  test-dynamo-compile:
+    description: "Test the Dynamo compile path"
     steps:
       - run:
-          name: Run Dynamo torch_compile E2E tests
+          name: Run Dynamo compile E2E tests
           command: |
             cd py/torch_tensorrt/dynamo/
             pushd test/
             pip3 install timm
             pip3 install transformers
-            pytest --junitxml=/tmp/artifacts/test_results/dynamo/torch_compile/test_results.xml --ir torch_compile
+            pytest --junitxml=/tmp/artifacts/test_results/dynamo/backend/test_results.xml --ir dynamo_compile
             popd
 
       - store_test_results:
@@ -1051,8 +1051,8 @@ jobs:
           command: pip3 install --pre /tmp/dist/x86_64-linux/*cp39-cp39*.whl
       # We install torch after torch-trt because pip automatically enforces the version constraint otherwise
       - dump-test-env
-      - test-dynamo-torch_compile
-      - test-dynamo-torch_compile-core
+      - test-dynamo-compile
+      - test-dynamo-compile-core
       - test-dynamo-fx_ts
 
   package-x86_64-linux:

py/torch_tensorrt/__init__.py

@@ -97,7 +97,7 @@ def _find_lib(name, paths):
 
 if version.parse(torch.__version__) >= version.parse("2.dev"):
     from torch_tensorrt import dynamo
-    from torch_tensorrt.dynamo import torch_compile
+    from torch_tensorrt.dynamo import backend
 
 
 def _register_with_torch():

py/torch_tensorrt/_compile.py

@@ -16,7 +16,7 @@ class _IRType(Enum):
     ts = 0
     fx = 1
     fx_ts_compat = 2
-    torch_compile = 3
+    dynamo_compile = 3
 
 
 class _ModuleType(Enum):
@@ -47,7 +47,7 @@ def _get_target_ir(module_type: _ModuleType, ir: str) -> _IRType:
 
     ir_targets_torchscript = any([ir == opt for opt in ["torchscript", "ts"]])
     ir_targets_fx = ir == "fx"
-    ir_targets_torch_compile = ir == "torch_compile"
+    ir_targets_dynamo_compile = ir == "dynamo_compile"
     ir_targets_fx_ts_compat = ir == "fx_ts_compat"
 
     if module_is_tsable and ir_targets_torchscript:
@@ -56,8 +56,8 @@ def _get_target_ir(module_type: _ModuleType, ir: str) -> _IRType:
         return _IRType.fx
     elif module_is_fxable and ir_targets_fx_ts_compat:
         return _IRType.fx_ts_compat
-    elif module_is_fxable and ir_targets_torch_compile:
-        return _IRType.torch_compile
+    elif module_is_fxable and ir_targets_dynamo_compile:
+        return _IRType.dynamo_compile
     else:
         if ir == "default":
             # Options are listed in order of preference
@@ -156,8 +156,8 @@ def compile(
             dynamic_batch=False,
             **kwargs,
         )
-    elif target_ir == _IRType.torch_compile:
-        return torch_tensorrt.dynamo.torch_compile(
+    elif target_ir == _IRType.dynamo_compile:
+        return torch_tensorrt.dynamo.compile(
             module, inputs=inputs, enabled_precisions=enabled_precisions, **kwargs
         )
     elif target_ir == _IRType.fx_ts_compat:

py/torch_tensorrt/dynamo/__init__.py

@@ -1,2 +1,2 @@
 from torch_tensorrt.dynamo import fx_ts_compat
-from .torch_compile import compile as torch_compile
+from .backend import compile

py/torch_tensorrt/dynamo/torch_compile/__init__.py renamed to py/torch_tensorrt/dynamo/backend/__init__.py

@@ -8,10 +8,10 @@
 from torch_tensorrt import EngineCapability, Device
 from torch_tensorrt.fx.utils import LowerPrecision
 
-from torch_tensorrt.dynamo.torch_compile._settings import CompilationSettings
-from torch_tensorrt.dynamo.torch_compile.utils import prepare_inputs, prepare_device
-from torch_tensorrt.dynamo.torch_compile.backends import tensorrt_backend
-from torch_tensorrt.dynamo.torch_compile._defaults import (
+from torch_tensorrt.dynamo.backend._settings import CompilationSettings
+from torch_tensorrt.dynamo.backend.utils import prepare_inputs, prepare_device
+from torch_tensorrt.dynamo.backend.backends import torch_tensorrt_backend
+from torch_tensorrt.dynamo.backend._defaults import (
     PRECISION,
     DEBUG,
     MAX_WORKSPACE_SIZE,
@@ -121,6 +121,6 @@ def create_backend(
     )
 
     return partial(
-        tensorrt_backend,
+        torch_tensorrt_backend,
         settings=settings,
     )

py/torch_tensorrt/dynamo/torch_compile/_defaults.py renamed to py/torch_tensorrt/dynamo/backend/_defaults.py

@@ -1,7 +1,7 @@
 from dataclasses import dataclass
 
 from torch_tensorrt.fx.utils import LowerPrecision
-from torch_tensorrt.dynamo.torch_compile._defaults import (
+from torch_tensorrt.dynamo.backend._defaults import (
     PRECISION,
     DEBUG,
     MAX_WORKSPACE_SIZE,

py/torch_tensorrt/dynamo/torch_compile/_settings.py renamed to py/torch_tensorrt/dynamo/backend/_settings.py

@@ -4,30 +4,42 @@
 from functools import partial
 import torch._dynamo as td
 
-from torch_tensorrt.dynamo.torch_compile._settings import CompilationSettings
-from torch_tensorrt.dynamo.torch_compile.lowering._decompositions import (
+from torch_tensorrt.dynamo.backend._settings import CompilationSettings
+from torch_tensorrt.dynamo.backend.lowering._decompositions import (
     get_decompositions,
 )
-from torch_tensorrt.dynamo.torch_compile.lowering._partition import (
+from torch_tensorrt.dynamo.backend.lowering._partition import (
     partition,
     get_submod_inputs,
 )
-from torch_tensorrt.dynamo.torch_compile.conversion import convert_module
+from torch_tensorrt.dynamo.backend.conversion import convert_module
 
 from torch._dynamo.backends.common import fake_tensor_unsupported
 
 from torch._functorch.aot_autograd import aot_module_simplified, make_boxed_compiler
 
 
-@td.register_backend(name="tensorrt")
+@td.register_backend(name="torch_tensorrt")
 @fake_tensor_unsupported
-def tensorrt_backend(
-    gm: torch.nn.Module,
+def torch_tensorrt_backend(
+    gm: torch.fx.GraphModule,
+    sample_inputs: Sequence[torch.Tensor],
+    settings: CompilationSettings = CompilationSettings(),
+):
+    DEFAULT_BACKEND = aot_torch_tensorrt_aten_backend
+
+    return DEFAULT_BACKEND(gm=gm, sample_inputs=sample_inputs, settings=settings)
+
+
+@td.register_backend(name="aot_torch_tensorrt_aten")
+@fake_tensor_unsupported
+def aot_torch_tensorrt_aten_backend(
+    gm: torch.fx.GraphModule,
     sample_inputs: Sequence[torch.Tensor],
     settings: CompilationSettings = CompilationSettings(),
 ):
     custom_backend = partial(
-        fx_dynamo_backend,
+        _pretraced_backend,
         settings=settings,
     )
 
@@ -40,14 +52,12 @@ def tensorrt_backend(
     )
 
 
-@td.register_backend(name="fx_tensorrt")
-@fake_tensor_unsupported
-def fx_dynamo_backend(
+def _pretraced_backend(
     gm: torch.fx.GraphModule,
-    example_inputs: Sequence[torch.Tensor],
+    sample_inputs: Sequence[torch.Tensor],
     settings: CompilationSettings = CompilationSettings(),
 ):
-    """Helper function to manage translation of FX module to TRT engines
+    """Helper function to manage translation of traced FX module to TRT engines
 
     Args:
         module: FX GraphModule to convert
@@ -57,9 +67,9 @@ def fx_dynamo_backend(
         Compiled FX GraphModule
     """
     try:
-        trt_compiled = compile_module(
+        trt_compiled = _compile_module(
             gm,
-            example_inputs,
+            sample_inputs,
             settings=settings,
         )
         return trt_compiled
@@ -72,12 +82,12 @@ def fx_dynamo_backend(
         return gm.forward
 
 
-def compile_module(
+def _compile_module(
     gm: torch.fx.GraphModule,
-    example_inputs: Sequence[torch.Tensor],
+    sample_inputs: Sequence[torch.Tensor],
     settings: CompilationSettings = CompilationSettings(),
 ) -> torch.fx.GraphModule:
-    """Compile an FX module
+    """Compile a traced FX module
 
     Includes: Partitioning + Conversion Phases
 
@@ -100,7 +110,7 @@ def compile_module(
 
         # Get submodule inputs
         submodule_inputs = get_submod_inputs(
-            partitioned_module, submodule, example_inputs
+            partitioned_module, submodule, sample_inputs
         )
 
         # Create TRT Module from submodule

py/torch_tensorrt/dynamo/torch_compile/backends.py renamed to py/torch_tensorrt/dynamo/backend/backends.py

@@ -0,0 +1,7 @@
+from torch_tensorrt.dynamo.backend.lowering._decompositions import (
+    get_decompositions,
+)
+from torch_tensorrt.dynamo.backend.lowering._partition import (
+    partition,
+    get_submod_inputs,
+)

py/torch_tensorrt/dynamo/torch_compile/conversion.py renamed to py/torch_tensorrt/dynamo/backend/conversion.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from torch_tensorrt.dynamo.torch_compile._defaults import MAX_NUM_TRT_ENGINES
+from torch_tensorrt.dynamo.backend._defaults import MAX_NUM_TRT_ENGINES
 from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
 from torch.fx.passes.operator_support import OperatorSupport
 

py/torch_tensorrt/dynamo/backend/lowering/__init__.py

@@ -1,4 +1,4 @@
-from torch_tensorrt.dynamo.torch_compile.utils import prepare_device, prepare_inputs
+from torch_tensorrt.dynamo.backend.utils import prepare_device, prepare_inputs
 from utils import same_output_format
 import torch_tensorrt
 import unittest

py/torch_tensorrt/dynamo/torch_compile/lowering/_decompositions.py renamed to py/torch_tensorrt/dynamo/backend/lowering/_decompositions.py

@@ -1,4 +1,4 @@
-from torch_tensorrt.dynamo.torch_compile.lowering import partition
+from torch_tensorrt.dynamo.backend.lowering import partition
 from torch.testing._internal.common_utils import run_tests, TestCase
 import torch
 from copy import deepcopy

py/torch_tensorrt/dynamo/torch_compile/lowering/_partition.py renamed to py/torch_tensorrt/dynamo/backend/lowering/_partition.py

@@ -0,0 +1,28 @@
+from torch.testing._internal.common_utils import run_tests, TestCase
+import torch
+import torch_tensorrt
+import torchvision.models as models
+from torch_tensorrt.dynamo.test.utils import COSINE_THRESHOLD, cosine_similarity
+
+
+class TestResNet18(TestCase):
+    def test_resnet18(ir):
+        model = models.resnet18(pretrained=True).eval().to("cuda")
+        input_ = torch.randn((1, 3, 224, 224)).to("cuda")
+
+        compile_spec = {
+            "inputs": [input_],
+            "enabled_precisions": {torch.float},
+            "pass_through_build_failures": True,
+        }
+
+        trt_mod = torch_tensorrt.dynamo.backend(model, **compile_spec)
+        cos_sim = cosine_similarity(model(input_), trt_mod(input_))
+        assert (
+            cos_sim > COSINE_THRESHOLD,
+            f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+
+if __name__ == "__main__":
+    run_tests()

py/torch_tensorrt/dynamo/torch_compile/test/test_compiler_utils.py renamed to py/torch_tensorrt/dynamo/backend/test/test_compiler_utils.py

@@ -2,10 +2,10 @@
 from functools import partial
 from typing import List, Sequence
 import torch
-from torch_tensorrt.dynamo.torch_compile.lowering._decompositions import (
+from torch_tensorrt.dynamo.backend.lowering._decompositions import (
     get_decompositions,
 )
-from torch_tensorrt.dynamo.torch_compile.lowering._partition import (
+from torch_tensorrt.dynamo.backend.lowering._partition import (
     partition,
 )
 

py/torch_tensorrt/dynamo/torch_compile/test/test_lowering.py renamed to py/torch_tensorrt/dynamo/backend/test/test_lowering.py

@@ -45,7 +45,7 @@ def prepare_inputs(
 
     else:
         raise ValueError(
-            f"Invalid input type {type(inputs)} encountered in the torch_compile input parsing. "
+            f"Invalid input type {type(inputs)} encountered in the dynamo_compile input parsing. "
             + "Allowed input types: {torch_tensorrt.Input, torch.Tensor, list, tuple, dict}"
         )
 

py/torch_tensorrt/dynamo/torch_compile/test/test_partitioning.py renamed to py/torch_tensorrt/dynamo/backend/test/test_partitioning.py

@@ -9,7 +9,7 @@ def pytest_addoption(parser):
         type=str,
         required=True,
         help="IR to compile with",
-        choices=["torch_compile", "fx_ts_compat"],
+        choices=["dynamo_compile", "fx_ts_compat"],
     )