[onert/python] Support dynamic shapes(on-the-fly)

runtime/onert/api/python/package/infer/session.py

@@ -1,6 +1,7 @@
 from typing import List, Union, Tuple, Dict
 import numpy as np
 import time
+import warnings
 from contextlib import contextmanager
 
 from ..native.libnnfw_api_pybind import infer, tensorinfo
@@ -22,19 +23,6 @@ def __init__(self, path: str, backends: str = "cpu") -> None:
         super().__init__(infer.nnfw_session(path, backends))
         self._prepared: bool = False
 
-        # TODO: Revise this after discussion to properly support dynamic shapes
-        # This is a temporary workaround to prevent prepare() errors when tensorinfo dims include -1
-        original_infos: List[tensorinfo] = self.get_inputs_tensorinfo()
-        fixed_infos: List[tensorinfo] = []
-        for info in original_infos:
-            dims = list(info.dims)
-            # replace -1 with 1
-            dims = [1 if d == -1 else d for d in dims]
-            info.dims = dims  # assume setter accepts a list
-            fixed_infos.append(info)
-        # update tensorinfo in session
-        self.update_inputs_tensorinfo(fixed_infos)
-
     def update_inputs_tensorinfo(self, new_infos: List[tensorinfo]) -> None:
         """
         Update all input tensors' tensorinfo at once.
@@ -89,20 +77,50 @@ def infer(
         """
         metrics: Dict[str, float] = {}
 
-        # Check if the session is prepared. If not, call prepare() and set_outputs() once.
-        if not self._prepared:
-            with self._time_block(metrics, 'prepare_time_ms', measure):
-                self.session.prepare()
-                self.set_outputs(self.session.output_size())
-                self._prepared = True
-
         # Verify that the number of provided inputs matches the session's expected input count.
         expected_input_size: int = self.session.input_size()
         if len(inputs_array) != expected_input_size:
             raise ValueError(
                 f"Expected {expected_input_size} input(s), but received {len(inputs_array)}."
             )
 
+        # Check if the session is prepared. If not, call prepare() and set_outputs() once.
+        if not self._prepared:
+            with self._time_block(metrics, 'prepare_time_ms', measure):
+                # On first call, fix any -1 dims to real input shapes and validate
+                original_infos = self.get_inputs_tensorinfo()
+                fixed_infos = []
+                for idx, info in enumerate(original_infos):
+                    input_shape = inputs_array[idx].shape
+                    new_dims = []
+                    static_dim_changed = False
+                    # only the first `info.rank` entries matter
+                    for j, d in enumerate(info.dims[:info.rank]):
+                        if d == -1:
+                            # replace dynamic dim with actual incoming shape
+                            new_dims.append(input_shape[j])
+                        elif d == input_shape[j]:
+                            # static dim must match the provided array
+                            new_dims.append(d)
+                        else:
+                            static_dim_changed = True
+
+                    if static_dim_changed:
+                        warnings.warn(
+                            f"infer() called with input {idx}'s shape={input_shape}, "
+                            f"which differs from model’s expected shape={tuple(info.dims)}. "
+                            "Ensure this is intended.", UserWarning)
+
+                    info.dims = new_dims
+                    fixed_infos.append(info)
+
+                # Update tensorinfo to optimize using it
+                self.update_inputs_tensorinfo(fixed_infos)
+
+                self.session.prepare()
+                self.set_outputs(self.session.output_size())
+                self._prepared = True
+
         # Configure input buffers using the current session's input size and provided data.
         with self._time_block(metrics, 'io_time_ms', measure):
             self.set_inputs(expected_input_size, inputs_array)

runtime/onert/sample/minimal-python/src/dynamic_shape_inference.py

@@ -0,0 +1,37 @@
+import numpy as np
+import random
+import sys
+from onert import infer
+
+
+def main(nnpackage_path, backends="cpu"):
+    # Create session and load nnpackage
+    session = infer.session(nnpackage_path, backends)
+
+    # Prepare input. Here we just allocate dummy input arrays.
+    input_infos = session.get_inputs_tensorinfo()
+
+    # Call infer() 10 times
+    for i in range(10):
+        dummy_inputs = []
+        for info in input_infos:
+            # Retrieve the dimensions list from tensorinfo property.
+            dims = list(info.dims)
+            # Replace -1 with a random value between 1 and 10
+            dims = [random.randint(1, 10) if d == -1 else d for d in dims]
+            # Build the shape tuple from tensorinfo dimensions.
+            shape = tuple(dims[:info.rank])
+            # Create a dummy numpy array filled with uniform random values in [0,1).
+            dummy_inputs.append(
+                np.random.uniform(low=0.0, high=1.0, size=shape).astype(info.dtype))
+
+        outputs = session.infer(dummy_inputs)
+        print(f"Inference run {i+1}/10 completed.")
+
+    print(f"nnpackage {nnpackage_path.split('/')[-1]} runs successfully.")
+    return
+
+
+if __name__ == "__main__":
+    argv = sys.argv[1:]
+    main(*argv)

runtime/onert/sample/minimal-python/src/static_shape_inference.py

@@ -18,12 +18,7 @@ def main(nnpackage_path, backends="cpu"):
         # We copy the current info and modify the rank and dims.
         # (Note: Depending on your model, you may want to modify additional dimensions.)
         new_shape = [10] + list(info.dims[1:info.rank])
-        info.rank = len(new_shape)
-        for i, dim in enumerate(new_shape):
-            info.dims[i] = dim
-        # For any remaining dimensions up to NNFW_MAX_RANK, set them to a default (1).
-        for i in range(len(new_shape), len(info.dims)):
-            info.dims[i] = 1
+        info.dims = new_shape
         new_input_infos.append(info)
 
     # Update all input tensorinfos in the session at once.