feat: custom binding names for out of runtime deployment

docsrc/tutorials/deployment/index.rst

@@ -11,4 +11,5 @@ complex-valued model support.
    serving_torch_tensorrt_with_triton
    cross_compile_windows
    Example: Cross-runtime Compilation for Windows <../_rendered_examples/dynamo/cross_runtime_compilation_for_windows>
+   Example: Naming engine I/O bindings <../_rendered_examples/dynamo/engine_converter_binding_names>
    distributed_inference

examples/dynamo/README.rst

@@ -26,4 +26,5 @@ Model Zoo
 * :ref:`_torch_export_sam2`: Compiling SAM2 model using AOT workflow (`ir=dynamo`)
 * :ref:`_torch_export_flux_dev`: Compiling FLUX.1-dev model using AOT workflow (`ir=dynamo`)
 * :ref:`debugger_example`: Debugging Torch-TensorRT Compilation
-* :ref:`torch_export_3d_rope`: Compiling a 3D RoPE video-transformer block with complex numerics support
+* :ref:`torch_export_3d_rope`: Compiling a 3D RoPE video-transformer block with complex numerics support
+* :ref:`engine_converter_binding_names`: Naming input / output bindings when emitting a raw serialized TRT engine

examples/dynamo/engine_converter_binding_names.py

@@ -0,0 +1,334 @@
+"""
+.. _engine_converter_binding_names:
+
+Naming Engine Bindings with ``convert_exported_program_to_serialized_trt_engine``
+=================================================================================
+
+When you use ``torch_tensorrt.dynamo.convert_exported_program_to_serialized_trt_engine``
+to produce a raw serialized TensorRT engine, the engine's binding names are
+determined by Torch-TensorRT's default policy:
+
+* **Inputs** get the FX placeholder names from the exported program (typically
+  the names of your ``forward()`` arguments).
+* **Outputs** get auto-generated names ``output0``, ``output1``, etc.
+
+Many production runtimes (Triton Inference Server, custom C++ harnesses,
+ONNX-style integrations) bind tensors by name rather than position, and the
+auto-generated names often don't line up with what the rest of the serving
+stack expects.  The engine converter exposes three keyword arguments that
+let you supply binding names shaped like your model's inputs and return
+value:
+
+* ``arg_input_binding_names`` — pytree of strings matching ``arg_inputs``
+* ``kwarg_input_binding_names`` — pytree of strings matching ``kwarg_inputs``
+* ``output_binding_names`` — pytree of strings matching the model's return
+
+The shape of each kwarg directly mirrors how you already pass the values
+themselves: ``arg_input_binding_names`` lines up with ``arg_inputs``,
+``kwarg_input_binding_names`` lines up with ``kwarg_inputs``.
+
+A note on "the return shape"
+----------------------------
+
+A Python function always returns exactly one value.  ``return a, b`` is a
+single tuple-shaped return value; ``return {"x": a, "y": b}`` is a single
+dict-shaped return value.  Whatever that value is, the exported program
+captures it as a pytree.  Its *leaves* — the individual tensors at the
+bottom of the structure — become engine bindings, and you supply names in
+the same pytree shape.  Inputs work the same way: ``arg_inputs`` is itself
+a pytree (a tuple of positional values, each of which can be a tensor or
+a nested collection of tensors); ``kwarg_inputs`` is a dict-shaped pytree.
+
+How it works
+------------
+
+The exported program already carries pytree specs (``args_spec`` for
+``arg_inputs``, ``kwargs_spec`` for ``kwarg_inputs``, ``out_spec`` for the
+return value) that fully describe the structure of inputs and outputs.
+When you provide binding names as a pytree of strings, Torch-TensorRT
+runs ``pytree.tree_flatten`` and compares the resulting ``TreeSpec``
+against the exported program's spec.  When they match, the flat list of
+names maps 1:1 to FX's flattened placeholder / output order — no runtime
+queue, no in-band validation, just an up-front structural check.
+"""
+
+import torch
+import torch_tensorrt
+from torch_tensorrt.dynamo._compiler import BindingNameMismatchError
+
+# %%
+import tensorrt as trt
+
+DEVICE = torch.device("cuda", 0)
+
+
+# %%
+# Helpers
+# --------
+#
+# A pair of small helpers: one reads binding names off a deserialized
+# engine, the other actually runs the engine via the native TRT Python
+# API.  The "execute via native TRT" path is what production deployments
+# use — the whole point of this API is that the binding names you supply
+# are the names you'll bind by at execution time, not just metadata in
+# the engine file.
+
+
+def deserialize(engine_bytes: bytes) -> trt.ICudaEngine:
+    runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))
+    return runtime.deserialize_cuda_engine(engine_bytes)
+
+
+def binding_names(engine: trt.ICudaEngine, mode: trt.TensorIOMode) -> list[str]:
+    return [
+        engine.get_tensor_name(i)
+        for i in range(engine.num_io_tensors)
+        if engine.get_tensor_mode(engine.get_tensor_name(i)) == mode
+    ]
+
+
+_TRT_TO_TORCH_DTYPE = {
+    trt.DataType.FLOAT: torch.float32,
+    trt.DataType.HALF: torch.float16,
+    trt.DataType.INT32: torch.int32,
+    trt.DataType.INT64: torch.int64,
+    trt.DataType.BOOL: torch.bool,
+    trt.DataType.BF16: torch.bfloat16,
+}
+
+
+def run_engine(engine: trt.ICudaEngine, named_inputs: dict) -> dict:
+    """Execute an engine via the native TRT Python API.
+
+    ``named_inputs`` is a {binding_name: contiguous CUDA tensor} dict.
+    Returns {binding_name: output tensor}.  Demonstrates that the
+    user-supplied binding names are what production C++/Python TRT
+    runtime code will bind by.
+    """
+    context = engine.create_execution_context()
+    for name, tensor in named_inputs.items():
+        context.set_input_shape(name, tuple(tensor.shape))
+        context.set_tensor_address(name, tensor.data_ptr())
+
+    outputs = {}
+    for i in range(engine.num_io_tensors):
+        name = engine.get_tensor_name(i)
+        if engine.get_tensor_mode(name) != trt.TensorIOMode.OUTPUT:
+            continue
+        shape = tuple(context.get_tensor_shape(name))
+        dtype = _TRT_TO_TORCH_DTYPE[engine.get_tensor_dtype(name)]
+        out = torch.empty(shape, dtype=dtype, device=DEVICE)
+        context.set_tensor_address(name, out.data_ptr())
+        outputs[name] = out
+
+    stream = torch.cuda.Stream(device=DEVICE)
+    with torch.cuda.stream(stream):
+        context.execute_async_v3(stream.cuda_stream)
+    stream.synchronize()
+    return outputs
+
+
+# %%
+# Case 1 — positional args, tuple-shaped return
+# ----------------------------------------------
+#
+# Start with the most common shape: ``forward(x)`` returning a 2-tuple.
+# ``arg_input_binding_names`` mirrors ``arg_inputs`` (a 1-tuple here);
+# ``output_binding_names`` mirrors the return tuple.
+
+
+class TwoHeads(torch.nn.Module):
+    def forward(self, x: torch.Tensor):
+        return torch.relu(x), torch.tanh(x)
+
+
+two_heads = TwoHeads().eval().cuda().half()
+x = torch.randn(2, 3, device=DEVICE, dtype=torch.float16)
+exported = torch.export.export(two_heads, (x,))
+
+engine_bytes = torch_tensorrt.dynamo.convert_exported_program_to_serialized_trt_engine(
+    exported,
+    arg_inputs=(x,),
+    arg_input_binding_names=("input_image",),
+    output_binding_names=("relu_out", "tanh_out"),
+    require_full_compilation=True,
+    min_block_size=1,
+    use_python_runtime=False,
+    immutable_weights=True,
+)
+engine = deserialize(engine_bytes)
+print("Case 1 inputs:", binding_names(engine, trt.TensorIOMode.INPUT))
+print("Case 1 outputs:", binding_names(engine, trt.TensorIOMode.OUTPUT))
+# Case 1 inputs: ['input_image']
+# Case 1 outputs: ['relu_out', 'tanh_out']
+
+# Run the engine through the native TRT API using the names we requested.
+trt_outs = run_engine(engine, {"input_image": x.contiguous()})
+with torch.no_grad():
+    ref_relu, ref_tanh = two_heads(x)
+torch.testing.assert_close(trt_outs["relu_out"], ref_relu, rtol=1e-2, atol=1e-2)
+torch.testing.assert_close(trt_outs["tanh_out"], ref_tanh, rtol=1e-2, atol=1e-2)
+print("Case 1 native TRT run matches PyTorch.")
+
+
+# %%
+# Case 2 — keyword-only inputs
+# ------------------------------
+#
+# When the model takes keyword arguments, you pass ``kwarg_inputs`` and
+# match its shape with ``kwarg_input_binding_names``.  Note we leave
+# ``arg_input_binding_names`` unset because ``arg_inputs`` is empty.
+
+
+class KwargOnly(torch.nn.Module):
+    def forward(self, image: torch.Tensor, positions: torch.Tensor):
+        return image + positions
+
+
+kwarg_only = KwargOnly().eval().cuda().half()
+image = torch.randn(2, 3, device=DEVICE, dtype=torch.float16)
+positions = torch.randn(2, 3, device=DEVICE, dtype=torch.float16)
+kw_exported = torch.export.export(
+    kwarg_only,
+    args=(),
+    kwargs={"image": image, "positions": positions},
+)
+
+engine_bytes = torch_tensorrt.dynamo.convert_exported_program_to_serialized_trt_engine(
+    kw_exported,
+    arg_inputs=(),
+    kwarg_inputs={"image": image, "positions": positions},
+    kwarg_input_binding_names={"image": "rgb_in", "positions": "pos_in"},
+    output_binding_names="combined",
+    require_full_compilation=True,
+    min_block_size=1,
+    use_python_runtime=False,
+    immutable_weights=True,
+)
+engine = deserialize(engine_bytes)
+print("Case 2 inputs:", sorted(binding_names(engine, trt.TensorIOMode.INPUT)))
+print("Case 2 outputs:", binding_names(engine, trt.TensorIOMode.OUTPUT))
+# Case 2 inputs: ['pos_in', 'rgb_in']
+# Case 2 outputs: ['combined']
+
+trt_outs = run_engine(
+    engine,
+    {"rgb_in": image.contiguous(), "pos_in": positions.contiguous()},
+)
+with torch.no_grad():
+    ref = kwarg_only(image=image, positions=positions)
+torch.testing.assert_close(trt_outs["combined"], ref, rtol=1e-2, atol=1e-2)
+print("Case 2 native TRT run matches PyTorch.")
+
+
+# %%
+# Case 3 — nested collections as inputs and outputs
+# --------------------------------------------------
+#
+# Inputs and outputs can be arbitrary nested collections of tensors —
+# tuples of dicts of tensors, lists of tuples, anything ``pytree`` can
+# flatten.  The binding-name kwargs follow the same nesting.  Here the
+# model takes a tuple of two cameras (each a dict of two tensors) and
+# returns a dict of feature stacks.
+
+
+class CameraTower(torch.nn.Module):
+    def forward(self, cameras: tuple, bias: torch.Tensor):
+        feats = []
+        for cam in cameras:
+            feats.append(cam["rgb"] + cam["depth"] + bias)
+        return {"primary": feats[0], "secondary": feats[1]}
+
+
+def _cam():
+    return {
+        "rgb": torch.randn(2, 3, device=DEVICE, dtype=torch.float16),
+        "depth": torch.randn(2, 3, device=DEVICE, dtype=torch.float16),
+    }
+
+
+camera_tower = CameraTower().eval().cuda().half()
+cameras = (_cam(), _cam())
+bias = torch.randn(2, 3, device=DEVICE, dtype=torch.float16)
+nested_exported = torch.export.export(camera_tower, args=(cameras, bias))
+
+engine_bytes = torch_tensorrt.dynamo.convert_exported_program_to_serialized_trt_engine(
+    nested_exported,
+    arg_inputs=(cameras, bias),
+    arg_input_binding_names=(
+        (
+            {"rgb": "cam0_rgb", "depth": "cam0_depth"},
+            {"rgb": "cam1_rgb", "depth": "cam1_depth"},
+        ),
+        "global_bias",
+    ),
+    output_binding_names={"primary": "p_feats", "secondary": "s_feats"},
+    require_full_compilation=True,
+    min_block_size=1,
+    use_python_runtime=False,
+    immutable_weights=True,
+)
+engine = deserialize(engine_bytes)
+print("Case 3 inputs:", sorted(binding_names(engine, trt.TensorIOMode.INPUT)))
+print("Case 3 outputs:", sorted(binding_names(engine, trt.TensorIOMode.OUTPUT)))
+# Case 3 inputs: ['cam0_depth', 'cam0_rgb', 'cam1_depth', 'cam1_rgb', 'global_bias']
+# Case 3 outputs: ['p_feats', 's_feats']
+
+trt_outs = run_engine(
+    engine,
+    {
+        "cam0_rgb": cameras[0]["rgb"].contiguous(),
+        "cam0_depth": cameras[0]["depth"].contiguous(),
+        "cam1_rgb": cameras[1]["rgb"].contiguous(),
+        "cam1_depth": cameras[1]["depth"].contiguous(),
+        "global_bias": bias.contiguous(),
+    },
+)
+with torch.no_grad():
+    ref = camera_tower(cameras, bias)
+torch.testing.assert_close(trt_outs["p_feats"], ref["primary"], rtol=1e-2, atol=1e-2)
+torch.testing.assert_close(trt_outs["s_feats"], ref["secondary"], rtol=1e-2, atol=1e-2)
+print("Case 3 native TRT run matches PyTorch.")
+
+
+# %%
+# Case 4 — structural validation
+# -------------------------------
+#
+# If the shape of any of the binding-name kwargs doesn't match the
+# exported program's spec, the converter raises
+# ``BindingNameMismatchError`` before any TensorRT network construction.
+# The error message shows the expected structure plus a leaf-position
+# listing — you can read the correct shape off the error and re-run.
+
+try:
+    torch_tensorrt.dynamo.convert_exported_program_to_serialized_trt_engine(
+        exported,
+        arg_inputs=(x,),
+        output_binding_names=("only_one",),  # wrong arity for the 2-tuple return
+        require_full_compilation=True,
+        min_block_size=1,
+        use_python_runtime=False,
+        immutable_weights=True,
+    )
+except BindingNameMismatchError as err:
+    print("Caught BindingNameMismatchError as expected.")
+    print(str(err).splitlines()[0])
+
+
+# %%
+# Notes
+# -----
+#
+# * The binding-name kwargs are *parallel* to the input kwargs they refer
+#   to: ``arg_input_binding_names`` matches ``arg_inputs``,
+#   ``kwarg_input_binding_names`` matches ``kwarg_inputs``.  Skip either
+#   one if the corresponding input slot is empty.
+# * Duplicate names within any individual list, or across inputs and
+#   outputs, are rejected at the API boundary — TensorRT requires
+#   binding names to be globally unique.
+# * This API is **only** available on
+#   ``convert_exported_program_to_serialized_trt_engine``.  ``compile()``
+#   and ``dynamo.compile()`` produce ``TorchTensorRTModule`` artifacts
+#   whose runtime depends on the default naming policy, so they
+#   intentionally don't expose this knob.