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new 8f1145d5d5 [DOCS] Update tutorial for exporting and loading back Relax
executables (#18412)
8f1145d5d5 is described below
commit 8f1145d5d5cbb9116196bdfd267d7f9aaba4bdc4
Author: Shushi Hong <[email protected]>
AuthorDate: Sun Nov 2 08:26:28 2025 -0500
[DOCS] Update tutorial for exporting and loading back Relax executables
(#18412)
* Replace relax.build with tvm.compile in export script
* Remove unnecessary print statement in export script
Remove print statement for skipping model conversion.
* Update output handling for TVM results
---
docs/how_to/tutorials/export_and_load_executable.py | 19 +++++++++++--------
1 file changed, 11 insertions(+), 8 deletions(-)
diff --git a/docs/how_to/tutorials/export_and_load_executable.py
b/docs/how_to/tutorials/export_and_load_executable.py
index 81e9bb0ef4..9665db48cb 100644
--- a/docs/how_to/tutorials/export_and_load_executable.py
+++ b/docs/how_to/tutorials/export_and_load_executable.py
@@ -89,9 +89,6 @@ if HAS_TORCH:
else: # pragma: no cover
TorchMLP = None # type: ignore[misc, assignment]
-if not RUN_EXAMPLE:
- print("Skip model conversion because PyTorch is unavailable or we are in
CI.")
-
if RUN_EXAMPLE:
torch_model = TorchMLP().eval()
example_args = (torch.randn(1, 1, 28, 28, dtype=torch.float32),)
@@ -126,7 +123,7 @@ if RUN_EXAMPLE:
built_mod = pipeline(mod)
# Build without params - we'll pass them at runtime
- executable = relax.build(built_mod, target=TARGET)
+ executable = tvm.compile(built_mod, target=TARGET)
library_path = ARTIFACT_DIR / "mlp_cpu.so"
executable.export_library(str(library_path),
workspace_dir=str(ARTIFACT_DIR))
@@ -176,7 +173,10 @@ if RUN_EXAMPLE:
# TVM returns Array objects for tuple outputs, access via indexing.
# For models imported from PyTorch, outputs are typically tuples (even for
single outputs).
# For ONNX models, outputs may be a single Tensor directly.
- result_tensor = tvm_output[0] if isinstance(tvm_output, (tuple, list))
else tvm_output
+ if isinstance(tvm_output, tvm.ir.Array) and len(tvm_output) > 0:
+ result_tensor = tvm_output[0]
+ else:
+ result_tensor = tvm_output
print("VM output shape:", result_tensor.shape)
print("VM output type:", type(tvm_output), "->", type(result_tensor))
@@ -209,7 +209,7 @@ if RUN_EXAMPLE:
#
# mod = from_exported_program(exported_program, keep_params_as_input=False)
# # Parameters are now embedded as constants in the module
-# executable = relax.build(built_mod, target=TARGET)
+# executable = tvm.compile(built_mod, target=TARGET)
# # Runtime: vm["main"](input) # No need to pass params!
#
# This creates a single-file deployment (only the ``.so`` is needed), but you
@@ -262,7 +262,10 @@ if RUN_EXAMPLE:
#
# # Step 6: Extract result (output may be tuple or single Tensor)
# # PyTorch models typically return tuples, ONNX models may return a single
Tensor
-# result = output[0] if isinstance(output, (tuple, list)) else output
+# if isinstance(tvm_output, tvm.ir.Array) and len(tvm_output) > 0:
+# result_tensor = tvm_output[0]
+# else:
+# result_tensor = tvm_output
#
# print("Prediction shape:", result.shape)
# print("Predicted class:", np.argmax(result.numpy()))
@@ -332,7 +335,7 @@ if RUN_EXAMPLE:
#
# # Step 1: Cross-compile for ARM target (on local machine)
# TARGET = tvm.target.Target("llvm -mtriple=aarch64-linux-gnu")
-# executable = relax.build(built_mod, target=TARGET)
+# executable = tvm.compile(built_mod, target=TARGET)
# executable.export_library("mlp_arm.so")
#
# # Step 2: Connect to remote device RPC server
