feat: Wrap ExportedPrograms transformations with an API, allow dynamo.compile to accept graphmodules.

docsrc/user_guide/saving_models.rst

@@ -22,8 +22,8 @@ The following code illustrates this approach.
     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
-    trt_script_model = torch.jit.trace(trt_gm, inputs)
-    torch.jit.save(trt_script_model, "trt_model.ts")
+    trt_traced_model = torchtrt.dynamo.serialize(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()
@@ -37,21 +37,19 @@ b) ExportedProgram
 
     import torch
     import torch_tensorrt
-    from torch_tensorrt.dynamo.export import transform, create_exported_program
 
     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
     # Transform and create an exported program
-    trt_gm = transform(trt_gm, inputs)
-    trt_exp_program = create_exported_program(trt_gm, call_spec, trt_gm.state_dict())
-    torch._export.save(trt_exp_program, "trt_model.ep")
+    trt_exp_program = torch_tensorrt.dynamo.serialize(trt_gm, inputs, 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 = torch.export.load("trt_model.ep")
     model(inputs)
 
-`torch_tensorrt.dynamo.export.transform` inlines the submodules within a GraphModule to their corresponding nodes and stiches all the nodes together. 
+`torch_tensorrt.dynamo.transform` 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

py/requirements.txt

@@ -1,9 +1,8 @@
 numpy
 packaging
 pybind11==2.6.2
---extra-index-url https://download.pytorch.org/whl/nightly/cu121
-torch>=2.1.0,<2.2.0
-torchvision>=0.16.0,<0.17.0
+torch==2.1.0
+torchvision==0.16.0
 --extra-index-url https://pypi.ngc.nvidia.com
 tensorrt==8.6.1
 pyyaml

py/torch_tensorrt/dynamo/__init__.py

@@ -16,3 +16,4 @@
         DYNAMO_CONVERTERS,
         dynamo_tensorrt_converter,
     )
+    from .export import serialize

py/torch_tensorrt/dynamo/compile.py

@@ -46,7 +46,7 @@
 
 
 def compile(
-    exported_program: ExportedProgram,
+    exported_program: Union[torch.fx.GraphModule, ExportedProgram],
     inputs: Any,
     *,
     device: Optional[Union[Device, torch.device, str]] = DEVICE,
@@ -86,7 +86,15 @@ def compile(
     inputs = prepare_inputs(inputs)
     device = to_torch_tensorrt_device(device)
 
-    gm = exported_program.module()
+    if isinstance(exported_program, torch.fx.GraphModule):
+        gm = exported_program
+    elif isinstance(exported_program, ExportedProgram):
+        gm = exported_program.module()
+    else:
+        raise AssertionError(
+            f"Input graph should either be an ExportedProgram or a GraphModule but got type {type(exported_program)}"
+        )
+
     logger.debug("Input graph: " + str(gm.graph))
 
     # Apply lowering on the graph module

py/torch_tensorrt/dynamo/export.py

@@ -1,6 +1,6 @@
 import copy
 import operator
-from typing import Any, Dict, Sequence, Tuple, Union, cast
+from typing import Any, Dict, Sequence, Tuple, cast
 
 import torch
 from torch._export.exported_program import CallSpec
@@ -10,28 +10,42 @@
 from torch_tensorrt.dynamo import partitioning
 
 
-def transform(
-    gm: torch.fx.GraphModule, inputs: Sequence[torch.Tensor]
-) -> torch.fx.GraphModule:
-    # Run shape analysis
-    _, outputs_map = partitioning.run_shape_analysis(gm, inputs)
-
-    # Inline TensorRT submodules
-    inline_trt_modules(gm, outputs_map)
-
-    # Inline pytorch submodules
-    inline_torch_modules(gm)
-
-    # Lift constant buffers and parameters in the graph
-    # torch.export serialization expects them to be lifted
-    lift_constant_pass(gm)
-
-    # Clean the graph
-    gm.delete_all_unused_submodules()
-    gm.graph.eliminate_dead_code()
-    gm.graph.lint()
-
-    return gm
+def serialize(
+    gm: torch.fx.GraphModule,
+    inputs: Sequence[torch.Tensor],
+    call_spec: CallSpec = None,
+    ir: str = "torchscript",
+) -> ExportedProgram:
+    if ir == "torchscript":
+        return torch.jit.trace(gm, inputs)
+    elif ir == "exported_program":
+        assert call_spec
+        # Run shape analysis
+        _, outputs_map = partitioning.run_shape_analysis(gm, inputs)
+
+        # Inline TensorRT submodules
+        inline_trt_modules(gm, outputs_map)
+
+        # Inline pytorch submodules
+        inline_torch_modules(gm)
+
+        # Lift constant buffers and parameters in the graph
+        # torch.export serialization expects them to be lifted
+        lift_constant_pass(gm)
+
+        # Clean the graph
+        gm.delete_all_unused_submodules()
+        gm.graph.eliminate_dead_code()
+        gm.graph.lint()
+
+        # Create an exported program with the TRT GraphModule
+        exp_program = create_trt_exp_program(gm, call_spec)
+
+        return exp_program
+    else:
+        raise ValueError(
+            "Invalid ir : {ir} provided for serialization. Options include torchscript | exported_program"
+        )
 
 
 def lift_constant_pass(trt_gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
@@ -115,7 +129,6 @@ def inline_torch_modules(gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
 
                 # Copy all nodes in the submodule into gm and
                 # store the output node of this submodule which is now present in gm
-
                 submodule_output = gm.graph.graph_copy(submodule.graph, val_map)
 
                 # Get their references (since we copied) in the parent graph (gm)
@@ -174,9 +187,7 @@ def copy_submodule_attributes(
 
 
 def create_trt_exp_program(
-    gm: torch.fx.GraphModule,
-    call_spec: CallSpec,
-    state_dict: Dict[str, Union[torch.Tensor, torch.nn.Parameter]],
+    gm: torch.fx.GraphModule, call_spec: CallSpec
 ) -> ExportedProgram:
     """Creates a new Exported Program. This function takes an torch.fx.GraphModule which has TRT engines
     and constructs an Exported Program object with the new IO node names, call_spec and state_dict
@@ -208,7 +219,7 @@ def create_trt_exp_program(
     )
 
     trt_exp_program = ExportedProgram(
-        gm, gm.graph, trt_graph_signature, call_spec, state_dict, {}, [], []
+        gm, gm.graph, trt_graph_signature, call_spec, gm.state_dict(), {}, [], []
     )
 
     return trt_exp_program

tests/py/dynamo/models/test_export_serde.py

@@ -6,7 +6,6 @@
 import torch_tensorrt as torchtrt
 import torchvision.models as models
 from torch._export.serde.serialize import deserialize, serialize
-from torch_tensorrt.dynamo.export import create_trt_exp_program, transform
 from torch_tensorrt.dynamo.utils import COSINE_THRESHOLD, cosine_similarity
 
 assertions = unittest.TestCase()
@@ -45,9 +44,8 @@ def forward(self, x):
 
     exp_program = torchtrt.dynamo.trace(model, **compile_spec)
     trt_gm = torchtrt.dynamo.compile(exp_program, **compile_spec)
-    trt_gm = transform(trt_gm, [input])
-    trt_exp_program = create_trt_exp_program(
-        trt_gm, exp_program.call_spec, trt_gm.state_dict()
+    trt_exp_program = torchtrt.dynamo.serialize(
+        trt_gm, [input], call_spec=exp_program.call_spec, ir="exported_program"
     )
     serialized_prog = serialize(trt_exp_program)
     deserialized_prog = deserialize(*serialized_prog)
@@ -100,11 +98,9 @@ def forward(self, x):
 
     exp_program = torchtrt.dynamo.trace(model, **compile_spec)
     trt_gm = torchtrt.dynamo.compile(exp_program, **compile_spec)
-    trt_gm = transform(trt_gm, [input])
-    trt_exp_program = create_trt_exp_program(
-        trt_gm, exp_program.call_spec, trt_gm.state_dict()
+    trt_exp_program = torchtrt.dynamo.serialize(
+        trt_gm, [input], call_spec=exp_program.call_spec, ir="exported_program"
     )
-
     serialized_prog = serialize(trt_exp_program)
     deserialized_prog = deserialize(*serialized_prog)
     # Check Pyt and TRT exported program outputs
@@ -161,11 +157,9 @@ def forward(self, x):
 
     exp_program = torchtrt.dynamo.trace(model, **compile_spec)
     trt_gm = torchtrt.dynamo.compile(exp_program, **compile_spec)
-    trt_gm = transform(trt_gm, [input])
-    trt_exp_program = create_trt_exp_program(
-        trt_gm, exp_program.call_spec, trt_gm.state_dict()
+    trt_exp_program = torchtrt.dynamo.serialize(
+        trt_gm, [input], call_spec=exp_program.call_spec, ir="exported_program"
     )
-
     torch._export.save(trt_exp_program, "/tmp/trt.ep")
     deser_trt_exp_program = torch._export.load("/tmp/trt.ep")
 
@@ -224,11 +218,9 @@ def forward(self, x):
 
     exp_program = torchtrt.dynamo.trace(model, **compile_spec)
     trt_gm = torchtrt.dynamo.compile(exp_program, **compile_spec)
-    trt_gm = transform(trt_gm, [input])
-    trt_exp_program = create_trt_exp_program(
-        trt_gm, exp_program.call_spec, trt_gm.state_dict()
+    trt_exp_program = torchtrt.dynamo.serialize(
+        trt_gm, [input], call_spec=exp_program.call_spec, ir="exported_program"
     )
-
     torch._export.save(trt_exp_program, "/tmp/trt.ep")
     deser_trt_exp_program = torch._export.load("/tmp/trt.ep")
 
@@ -270,9 +262,8 @@ def test_resnet18_save_load(ir):
 
     exp_program = torchtrt.dynamo.trace(model, **compile_spec)
     trt_gm = torchtrt.dynamo.compile(exp_program, **compile_spec)
-    trt_gm = transform(trt_gm, [input])
-    trt_exp_program = create_trt_exp_program(
-        trt_gm, exp_program.call_spec, trt_gm.state_dict()
+    trt_exp_program = torchtrt.dynamo.serialize(
+        trt_gm, [input], call_spec=exp_program.call_spec, ir="exported_program"
     )
     torch._export.save(trt_exp_program, "/tmp/trt.ep")
     deser_trt_exp_program = torch._export.load("/tmp/trt.ep")
@@ -291,59 +282,3 @@ def test_resnet18_save_load(ir):
         cos_sim > COSINE_THRESHOLD,
         msg=f"test_resnet18_save_load TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
     )
-
-
-# Enable this test once this issue is resolved https://github.com/pytorch/TensorRT/issues/2341
-# @pytest.mark.unit
-# def test_hybrid_conv_fallback(ir):
-#     """
-#     This tests export save and load functionality on a hybrid
-#     model where a conv (a weighted layer)  has been forced to fallback to Pytorch.
-#     """
-
-#     class MyModule(torch.nn.Module):
-#         def __init__(self):
-#             super().__init__()
-#             self.conv = torch.nn.Conv2d(3, 16, 3, stride=1, bias=True)
-#             self.relu = torch.nn.ReLU()
-
-#         def forward(self, x):
-#             conv = self.conv(x)
-#             relu = self.relu(conv)
-#             mul = relu * 0.5
-#             return mul
-
-#     model = MyModule().eval().cuda()
-#     input = torch.randn((1, 3, 224, 224)).to("cuda")
-
-#     compile_spec = {
-#         "inputs": [
-#             torchtrt.Input(
-#                 input.shape, dtype=torch.float, format=torch.contiguous_format
-#             )
-#         ],
-#         "ir": ir,
-#         "min_block_size": 1,
-#         "torch_executed_ops": "torch.ops.aten.convolution.default",
-#     }
-
-#     trt_exp_program = torchtrt.compile(model, **compile_spec)
-#     torch._export.save(trt_exp_program, "/tmp/trt.ep")
-#     deser_trt_exp_program = torch._export.load("/tmp/trt.ep")
-
-#     outputs_pyt = model(input)
-#     outputs_trt = trt_exp_program(input)
-#     for idx in range(len(outputs_pyt)):
-#         cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt[idx])
-#         assertions.assertTrue(
-#             cos_sim > COSINE_THRESHOLD,
-#             msg=f"test_base_full_compile_multiple_outputs TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
-#         )
-
-#     outputs_trt_deser = deser_trt_exp_program(input)
-#     for idx in range(len(outputs_pyt)):
-#         cos_sim = cosine_similarity(outputs_pyt[idx], outputs_trt_deser[idx])
-#         assertions.assertTrue(
-#             cos_sim > COSINE_THRESHOLD,
-#             msg=f"test_base_full_compile_save_load TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
-#         )