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Error Saving YOLOv8 Model After CoreML Conversion
🐛 Describe the bug
I'm trying to export the ultralytics YOLOv8 model using the CoreML backend, but I'm getting an error when saving the serialized lowered module.
Btw, I manage to save in the portable format.
Here's the code I'm using:
from executorch.backends.apple.coreml.compiler import CoreMLBackend
from executorch.exir.backend.backend_api import to_backend
def generate_compile_specs_from_args(fp16 = False, compile = False):
# model type will change depending on the compile option
model_type = CoreMLBackend.MODEL_TYPE.MODEL
if compile:
model_type = CoreMLBackend.MODEL_TYPE.COMPILED_MODEL
# precision can be FLOAT16 or FLOAT32
compute_precision = ct.precision.FLOAT16 if fp16 else ct.precision.FLOAT32
# compute_unit: sets where the model should run, CPU, GPU, NE (neural engine), all.
compute_unit = ct.ComputeUnit["ALL"]
return CoreMLBackend.generate_compile_specs(
compute_precision=compute_precision,
compute_unit=compute_unit,
model_type=model_type,
)
def lower_to_coreml_backend(to_be_lowered_module, fp16, compile):
return to_backend(
CoreMLBackend.__name__,
to_be_lowered_module,
generate_compile_specs_from_args(fp16, compile),
)
model = YOLO(f"{MODEL_NAME}").model #.model is the actual model object
with torch.inference_mode():
model.to("cpu")
for p in model.parameters():
p.requires_grad = False
model.float()
model.eval()
model = model.fuse()
y = None
im = torch.zeros(1, 3, *MODEL_SIZE).to("cpu")
for _ in range(2):
y = model(im) # dry runs
example_args = (torch.randn(1, 3, *MODEL_SIZE),)
pre_autograd_aten_dialect = capture_pre_autograd_graph(model, example_args)
aten_dialect = torch.export.export(pre_autograd_aten_dialect, example_args)
# Export and lower the module to Edge Dialect
edge_program = to_edge(aten_dialect)
# Lower the module to CoreML backend
to_be_lowered_module = edge_program.exported_program()
lowered_module = lower_to_coreml_backend(to_be_lowered_module, fp16=False, compile=False)
# Serialize and save it to a file
with open("models/yolo_executorch_coreml.pte", "wb") as file:
file.write(lowered_module.buffer())
It's giving me the following error:
{
"name": "Exception",
"message": "An error occurred when running the 'SpecPropPass' pass after the following passes: []",
"stack": "---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py:271, in PassManager.__call__(self, module)
270 try:
--> 271 res = fn(module)
273 if not isinstance(res, PassResult) and not hasattr(
274 res, \"graph_module\"
275 ):
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/torch/fx/passes/infra/pass_base.py:41, in PassBase.__call__(self, graph_module)
40 self.requires(graph_module)
---> 41 res = self.call(graph_module)
42 self.ensures(graph_module)
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/pass_base.py:572, in _ExportPassBase.call(self, graph_module)
571 with fake_tensor_mode, dispatcher_mode: # type: ignore[assignment, union-attr]
--> 572 result = self.call_submodule(graph_module, tuple(inputs))
574 return result
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/pass_base.py:658, in ExportPass.call_submodule(self, graph_module, inputs)
655 def call_submodule(
656 self, graph_module: fx.GraphModule, inputs: Tuple[Argument, ...]
657 ) -> PassResult:
--> 658 res = super().call_submodule(graph_module, inputs)
660 def preserve_original_ph_meta_val(
661 gm: torch.fx.GraphModule, new_gm: torch.fx.GraphModule
662 ) -> None:
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/pass_base.py:535, in _ExportPassBase.call_submodule(self, graph_module, inputs)
534 with fx_traceback.preserve_node_meta():
--> 535 interpreter.run(*inputs_data)
537 new_graph_module = torch.fx.GraphModule(self.tracer.root, self.tracer.graph)
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/torch/fx/interpreter.py:146, in Interpreter.run(self, initial_env, enable_io_processing, *args)
145 try:
--> 146 self.env[node] = self.run_node(node)
147 except Exception as e:
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/pass_base.py:375, in _ExportPassBase.ExportInterpreter.run_node(self, n)
374 self.callback.node_debug_str = n.format_node()
--> 375 return super().run_node(n)
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/torch/fx/interpreter.py:203, in Interpreter.run_node(self, n)
202 assert isinstance(kwargs, dict)
--> 203 return getattr(self, n.op)(n.target, args, kwargs)
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/pass_base.py:605, in ExportPass.ExportInterpreter.call_function(self, target, args, kwargs)
604 value, key = args
--> 605 return self.callback.call_getitem(value, key, meta)
606 elif isinstance(target, EdgeOpOverload):
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/passes/spec_prop_pass.py:100, in SpecPropPass.call_getitem(self, value, key, meta)
99 meta[\"spec\"] = value.node.meta[\"spec\"][key]
--> 100 return super().call_getitem(value, key, meta)
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/pass_base.py:517, in _ExportPassBase.call_getitem(self, value, key, meta)
514 def call_getitem(
515 self, value: ProxyValue, key: int, meta: NodeMetadata
516 ) -> ProxyValue:
--> 517 return self._fx(\"call_function\", operator.getitem, (value, key), {}, meta)
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/pass_base.py:397, in _ExportPassBase._fx(self, kind, target, args, kwargs, meta)
394 args_data, kwargs_data = pytree.tree_map_only(
395 ProxyValue, lambda x: x.data, (args, kwargs)
396 )
--> 397 res_data = getattr(self.interpreter, kind)(target, args_data, kwargs_data)
398 args_proxy, kwargs_proxy = pytree.tree_map_only(
399 ProxyValue, lambda x: x.proxy, (args, kwargs)
400 )
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/torch/fx/interpreter.py:275, in Interpreter.call_function(self, target, args, kwargs)
274 # Execute the function and return the result
--> 275 return target(*args, **kwargs)
IndexError: tuple index out of range
While executing %getitem_25 : [num_users=1] = call_function[target=operator.getitem](args = (%executorch_call_delegate, 2), kwargs = {})
Original traceback:
None
The above exception was the direct cause of the following exception:
Exception Traceback (most recent call last)
Cell In[24], line 41
39 # Serialize and save it to a file
40 with open(\"models/yolo_executorch_coreml.pte\", \"wb\") as file:
---> 41 file.write(lowered_module.buffer())
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/lowered_backend_module.py:149, in LoweredBackendModule.buffer(self, extract_delegate_segments, segment_alignment, constant_tensor_alignment, delegate_alignment)
143 \"\"\"
144 Returns a buffer containing the serialized ExecuTorch binary.
145 \"\"\"
146 # TODO(T181463742): avoid calling bytes(..) which incurs large copies.
147 out = bytes(
148 _serialize_pte_binary(
--> 149 program=self.program(),
150 extract_delegate_segments=extract_delegate_segments,
151 segment_alignment=segment_alignment,
152 constant_tensor_alignment=constant_tensor_alignment,
153 delegate_alignment=delegate_alignment,
154 )
155 )
156 return out
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/lowered_backend_module.py:322, in LoweredBackendModule.program(self, emit_stacktrace)
302 # Double check the ExportedProgram data(especially everything except graph) is good
303 exported_program = ExportedProgram(
304 root=lowered_exported_program.graph_module,
305 graph=lowered_exported_program.graph,
(...)
320 verifier=lowered_exported_program.verifier,
321 )
--> 322 exported_program = _transform(
323 exported_program, SpecPropPass(), MemoryPlanningPass(\"greedy\")
324 )
325 emitted_program = emit_program(
326 exported_program, emit_stacktrace=emit_stacktrace
327 ).program
328 return emitted_program
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/executorch/exir/program/_program.py:179, in _transform(self, *passes)
177 def _transform(self, *passes: PassType) -> \"ExportedProgram\":
178 pm = PassManager(list(passes))
--> 179 res = pm(self.graph_module)
180 transformed_gm = res.graph_module if res is not None else self.graph_module
181 assert transformed_gm is not None
File ~/projects/object_detection_ios_comprehensive/yolov5_yolov8_ultralytics_to_executorch/.executorch/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py:297, in PassManager.__call__(self, module)
292 prev_pass_names = [
293 p.__name__ if inspect.isfunction(p) else type(p).__name__
294 for p in self.passes[:i]
295 ]
296 msg = f\"An error occurred when running the '{fn_name}' pass after the following passes: {prev_pass_names}\"
--> 297 raise Exception(msg) from e # noqa: TRY002
299 # If the graph no longer changes, then we can stop running these passes
300 overall_modified = overall_modified or modified
Exception: An error occurred when running the 'SpecPropPass' pass after the following passes: []"
}
Versions
Collecting environment information... PyTorch version: 2.4.0 Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A
OS: macOS 14.5 (arm64) GCC version: Could not collect Clang version: 15.0.0 (clang-1500.3.9.4) CMake version: version 3.30.1 Libc version: N/A
Python version: 3.10.14 (main, Mar 19 2024, 21:46:16) [Clang 15.0.0 (clang-1500.3.9.4)] (64-bit runtime) Python platform: macOS-14.5-arm64-arm-64bit Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True
CPU: Apple M3
Versions of relevant libraries: [pip3] executorch==0.3.0a0+ca8e0d2 [pip3] executorchcoreml==0.0.1 [pip3] numpy==1.26.4 [pip3] torch==2.4.0 [pip3] torchaudio==2.4.0 [pip3] torchsr==1.0.4 [pip3] torchvision==0.19.0 [conda] Could not collect
Hey guys, any idea what I can triy here, or any debug it might give me some insights?
Hey thank you for trying out, can you try the partitioner flow? It will partially lower the model and the failing part will fall back to cpu
I changed the above code to:
edge_program = to_edge(aten_dialect)
lowered_module = edge_program.to_backend(
CoreMLPartitioner(
skip_ops_for_coreml_delegation=["aten.convolution.default"]
)
)
with open("models/yolo_executorch_coreml.pte", "wb") as file:
file.write(lowered_module.buffer())
got this error:
AttributeError: 'EdgeProgramManager' object has no attribute 'buffer'
to_backend is returning something different: EdgeProgramManager.
Maybe you guys can dumb it down for me, I'm still not understanding all the pipelines that executorch provides.
Try these lines?
edge_program = to_edge(aten_dialect)
edge_program = edge_program.to_backend(
CoreMLPartitioner(
skip_ops_for_coreml_delegation=["aten.convolution.default"]
)
)
et_program = edge_manager.to_executorch()
with open("models/yolo_executorch_coreml.pte", "wb") as file:
file.write(et_program.buffer)