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Description
Bug description
Steps/Code to reproduce bug
I run this example from Merlin Models. I run it all the way (and inclusive) to the cell where I train the model:
model.compile(run_eagerly=False, optimizer='adam', loss="categorical_crossentropy")
model.fit(train_set_processed, batch_size=64, epochs=5, pre=mm.SequenceMaskRandom(schema=seq_schema, target=target, masking_prob=0.3, transformer=transform
Subsequently, I attempt to exporting the ensemble to perform inference on triton using the following code:
from merlin.systems.dag.ops.tensorflow import PredictTensorflow
from merlin.systems.dag.ensemble import Ensemble
from merlin.systems.dag.ops.workflow import TransformWorkflow
inf_ops = wf.input_schema.column_names >> TransformWorkflow(wf) >> PredictTensorflow(model)
ensemble = Ensemble(inf_ops, wf.input_schema)
ensemble.export('/workspace/models_for_benchmarking');
The following error occurs:
Expected behavior
The error doesn't occur and I am able to export the ensemble.
Environment details
I am running the 22.12 tensorflow container with all repos updated to current main.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[24], line 8
5 inf_ops = wf.input_schema.column_names >> TransformWorkflow(wf) >> PredictTensorflow(model)
7 ensemble = Ensemble(inf_ops, wf.input_schema)
----> 8 ensemble.export('/workspace/models_for_benchmarking');
File /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/ensemble.py:153, in Ensemble.export(self, export_path, runtime, **kwargs)
148 """
149 Write out an ensemble model configuration directory. The exported
150 ensemble is designed for use with Triton Inference Server.
151 """
152 runtime = runtime or TritonExecutorRuntime()
--> 153 return runtime.export(self, export_path, **kwargs)
File /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/runtimes/triton/runtime.py:133, in TritonExecutorRuntime.export(self, ensemble, path, version, name)
131 node_id = node_id_table.get(node, None)
132 if node_id is not None:
--> 133 node_config = node.export(path, node_id=node_id, version=version)
134 if node_config is not None:
135 node_configs.append(node_config)
File /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/node.py:50, in InferenceNode.export(self, output_path, node_id, version)
27 def export(
28 self,
29 output_path: Union[str, os.PathLike],
30 node_id: int = None,
31 version: int = 1,
32 ):
33 """
34 Export a Triton config directory for this node.
35
(...)
48 Triton model config corresponding to this node.
49 """
---> 50 return self.op.export(
51 output_path,
52 self.input_schema,
53 self.output_schema,
54 node_id=node_id,
55 version=version,
56 )
File /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/runtimes/triton/ops/workflow.py:170, in TransformWorkflowTriton.export(self, path, input_schema, output_schema, params, node_id, version)
167 node_export_path = pathlib.Path(path) / node_name
168 node_export_path.mkdir(parents=True, exist_ok=True)
--> 170 backend_model_config = _generate_nvtabular_model(
171 modified_workflow,
172 node_name,
173 node_export_path,
174 sparse_max=self.op.sparse_max,
175 max_batch_size=self.op.max_batch_size,
176 cats=self.op.cats,
177 conts=self.op.conts,
178 )
180 return backend_model_config
File /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/runtimes/triton/ops/workflow.py:206, in _generate_nvtabular_model(workflow, name, output_path, version, output_model, max_batch_size, sparse_max, backend, cats, conts)
195 """converts a workflow to a triton mode
196 Parameters
197 ----------
(...)
203 Names of the continuous columns
204 """
205 workflow.save(os.path.join(output_path, str(version), "workflow"))
--> 206 config = _generate_nvtabular_config(
207 workflow,
208 name,
209 output_path,
210 output_model,
211 max_batch_size,
212 sparse_max=sparse_max,
213 backend=backend,
214 cats=cats,
215 conts=conts,
216 )
218 # copy the model file over. note that this isn't necessary with the c++ backend, but
219 # does provide us to use the python backend with just changing the 'backend' parameter
220 with importlib.resources.path(
221 "merlin.systems.triton.models", "workflow_model.py"
222 ) as workflow_model:
File /usr/local/lib/python3.8/dist-packages/merlin/systems/dag/runtimes/triton/ops/workflow.py:289, in _generate_nvtabular_config(workflow, name, output_path, output_model, max_batch_size, sparse_max, backend, cats, conts)
287 else:
288 for col_name, col_schema in workflow.input_schema.column_schemas.items():
--> 289 _add_model_param(col_schema, model_config.ModelInput, config.input)
291 for col_name, col_schema in workflow.output_schema.column_schemas.items():
292 if sparse_max and col_name in sparse_max.keys():
293 # this assumes max_sequence_length is equal for all output columns
File /usr/local/lib/python3.8/dist-packages/merlin/systems/triton/export.py:44, in _add_model_param(col_schema, paramclass, params, dims)
37 params.append(
38 paramclass(
39 name=col_schema.name + "__offsets", data_type=model_config.TYPE_INT32, dims=dims
40 )
41 )
42 else:
43 params.append(
---> 44 paramclass(name=col_schema.name, data_type=_convert_dtype(col_schema.dtype), dims=dims)
45 )
File /usr/local/lib/python3.8/dist-packages/merlin/systems/triton/export.py:59, in _convert_dtype(dtype)
57 return model_config.TYPE_STRING
58 else:
---> 59 raise ValueError(f"Can't convert {dtype} to a Triton dtype")
ValueError: Can't convert datetime64[ns] to a Triton dtype