simplify jobs script

src/qualcomm/aihub_jobs.py

@@ -1,32 +1,26 @@
 from pathlib import Path
-from typing import Any
+from typing import Any, TypedDict
+
+import qai_hub.hub as hub
+from qai_hub.client import CompileJob, Device, InferenceJob, Model, ProfileJob, QuantizeDtype, QuantizeJob
 
 
-def _hub() -> Any:
-    import qai_hub as hub
-
-    return hub
+class ModelJobResult(TypedDict):
+    job: CompileJob | QuantizeJob
+    job_id: str
+    model: Model
+    model_id: str
 
 
-def _id(obj: Any) -> str:
-    for attr in ("model_id", "job_id", "id"):
-        value = getattr(obj, attr, None)
-        if value:
-            return str(value)
-    return str(obj)
+class InferenceJobResult(TypedDict):
+    job: InferenceJob
+    job_id: str
+    outputs: Any
 
 
-def _target_model(job: Any) -> Any:
-    if hasattr(job, "get_target_model"):
-        return job.get_target_model()
-    model = getattr(job, "target_model", None)
-    if model is not None:
-        return model
-    return job
-
-
-def get_model(model_id: str) -> Any:
-    return _hub().get_model(model_id)
+class ProfileJobResult(TypedDict):
+    job: ProfileJob
+    job_id: str
 
 
 def _dataset_entries(inputs: dict[str, Any]) -> dict[str, list[Any]]:
@@ -41,8 +35,7 @@ def submit_compile_job(
     options: str | None = None,
     job_name: str | None = None,
     model_name: str | None = None,
-) -> dict[str, Any]:
-    hub = _hub()
+) -> ModelJobResult:
     compile_options = f"--target_runtime {target_runtime}"
     if options:
         compile_options = f"{compile_options} {options}"
@@ -52,22 +45,22 @@ def submit_compile_job(
         model_arg = str(model)
     elif isinstance(model, str):
         candidate = Path(model)
-        model_arg = model if candidate.exists() or candidate.suffix else get_model(model)
+        model_arg = model if candidate.exists() or candidate.suffix else hub.get_model(model)
 
     if model_name and isinstance(model_arg, str) and Path(model_arg).exists():
         model_arg = hub.upload_model(model_arg, name=model_name)
 
     job = hub.submit_compile_job(
         model=model_arg,
-        device=hub.Device(device_name),
+        device=Device(device_name),
         name=job_name,
         input_specs=input_specs,
         options=compile_options,
     )
-    target_model = _target_model(job)
+    target_model = job.get_target_model()
     if target_model is None:
-        raise RuntimeError(f"Compile job {_id(job)} did not produce a target model.")
-    return {"job": job, "job_id": _id(job), "model": target_model, "model_id": _id(target_model)}
+        raise RuntimeError(f"Compile job {job.job_id} did not produce a target model.")
+    return {"job": job, "job_id": str(job.job_id), "model": target_model, "model_id": str(target_model.model_id)}
 
 
 def submit_inference_job(
@@ -76,18 +69,17 @@ def submit_inference_job(
     inputs: dict[str, Any],
     output_dir: str | Path,
     job_name: str | None = None,
-) -> dict[str, Any]:
-    hub = _hub()
+) -> InferenceJobResult:
     job = hub.submit_inference_job(
-        model=get_model(model_id),
-        device=hub.Device(device_name),
+        model=hub.get_model(model_id),
+        device=Device(device_name),
         inputs=_dataset_entries(inputs),
         name=job_name,
     )
     out = Path(output_dir)
     out.mkdir(parents=True, exist_ok=True)
     data = job.download_output_data(str(out))
-    return {"job": job, "job_id": _id(job), "outputs": data}
+    return {"job": job, "job_id": str(job.job_id), "outputs": data}
 
 
 def submit_profile_job(
@@ -95,15 +87,14 @@ def submit_profile_job(
     device_name: str,
     options: str | None = None,
     job_name: str | None = None,
-) -> dict[str, Any]:
-    hub = _hub()
+) -> ProfileJobResult:
     job = hub.submit_profile_job(
-        model=get_model(model_id),
-        device=hub.Device(device_name),
+        model=hub.get_model(model_id),
+        device=Device(device_name),
         name=job_name,
         options=options or "",
     )
-    return {"job": job, "job_id": _id(job)}
+    return {"job": job, "job_id": str(job.job_id)}
 
 
 def submit_quantize_job(
@@ -112,33 +103,27 @@ def submit_quantize_job(
     options: str | None = None,
     job_name: str | None = None,
     model_name: str | None = None,
-) -> dict[str, Any]:
-    hub = _hub()
+) -> ModelJobResult:
     model_arg = str(model)
     if model_name and Path(model_arg).exists():
         model_arg = hub.upload_model(model_arg, name=model_name)
     job = hub.submit_quantize_job(
         model=model_arg,
         calibration_data=_dataset_entries(calibration_data),
-        weights_dtype=hub.QuantizeDtype.INT8,
-        activations_dtype=hub.QuantizeDtype.INT8,
+        weights_dtype=QuantizeDtype.INT8,
+        activations_dtype=QuantizeDtype.INT8,
         name=job_name,
         options=options or "",
     )
-    target_model = _target_model(job)
+    target_model = job.get_target_model()
     if target_model is None:
-        raise RuntimeError(f"Quantize job {_id(job)} did not produce a target model.")
-    return {"job": job, "job_id": _id(job), "model": target_model, "model_id": _id(target_model)}
+        raise RuntimeError(f"Quantize job {job.job_id} did not produce a target model.")
+    return {"job": job, "job_id": str(job.job_id), "model": target_model, "model_id": str(target_model.model_id)}
 
 
 def download_model(model_id: str, output_path: str | Path) -> str:
     dest = Path(output_path)
     dest.parent.mkdir(parents=True, exist_ok=True)
-    model = get_model(model_id)
-    if hasattr(model, "download"):
+    model = hub.get_model(model_id)
     result = model.download(str(dest))
     return str(result or dest)
-    if hasattr(model, "download_model"):
-        result = model.download_model(str(dest))
-        return str(result or dest)
-    raise RuntimeError("AI Hub model object does not expose a download method.")