Allow Passing in Size of Dynamic Dimensions to Inference Function

fx2ait/fx2ait/find_batch_size_dim.py

@@ -21,55 +21,59 @@ def find_batch_size_dim(
     inputs: Any,
     can_non_first_dim_be_dynamic: bool = True,
     can_dim_value_one_be_dynamic: bool = True,
+    dynamic_size: int = -1,
     # pyre-fixme Invalid type [31]
 ) -> []:
     if isinstance(inputs, torch.Tensor) or len(inputs) <= 1:
         return [0]
-    shapes = [i.shape for i in inputs]
-    frequency_map = {}
-    position_scores = {}
-    first_dims = set()
-    for shape in shapes:
-        if len(shape) < 2:
-            # By pass for rank-1 tensors. MRS model has rank-1 tensor carry no batch_size info
-            continue
-        # Dedup shape value for single tensor
-        first_dims.add(shape[0])
-        seen_dims = set()
-        valid_len = len(shape) if can_non_first_dim_be_dynamic else 1
-        for i in range(valid_len):
-            dim = shape[i]
-            if dim not in seen_dims:
-                frequency_map[dim] = frequency_map.get(dim, 0) + 1
-                position_scores[dim] = position_scores.get(dim, 0) + i
-                seen_dims.add(dim)
 
-    if len(first_dims) == 1:
-        # first dim is the same in every input: we use it as batch_size
-        batch_size = first_dims.pop()
-    elif frequency_map:
-        # first dims are different: we use the most frequent dim as batch_size
-        # if there is more than 1 most frequent dim, we choose the one with the
-        # lowest position score (i.e., the leftmost of the most frequent ones)
-        sorted_frequency = sorted(
-            frequency_map.items(),
-            key=lambda x: (-x[1], position_scores[x[0]]),
-        )
-        if len(sorted_frequency) > 1:
-            if not can_dim_value_one_be_dynamic and sorted_frequency[0][0] == 1:
-                # It's often that dim value one indicates a non-dynamic dimension.
-                # If the user says so, we pick the second most frequent value.
-                batch_size = sorted_frequency[1][0]
+    if dynamic_size > 0:
+        batch_size = dynamic_size
+    else:
+        shapes = [i.shape for i in inputs]
+        frequency_map = {}
+        position_scores = {}
+        first_dims = set()
+        for shape in shapes:
+            if len(shape) < 2:
+                # By pass for rank-1 tensors. MRS model has rank-1 tensor carry no batch_size info
+                continue
+            # Dedup shape value for single tensor
+            first_dims.add(shape[0])
+            seen_dims = set()
+            valid_len = len(shape) if can_non_first_dim_be_dynamic else 1
+            for i in range(valid_len):
+                dim = shape[i]
+                if dim not in seen_dims:
+                    frequency_map[dim] = frequency_map.get(dim, 0) + 1
+                    position_scores[dim] = position_scores.get(dim, 0) + i
+                    seen_dims.add(dim)
+        if len(first_dims) == 1:
+            # first dim is the same in every input: we use it as batch_size
+            batch_size = first_dims.pop()
+        elif frequency_map:
+            # first dims are different: we use the most frequent dim as batch_size
+            # if there is more than 1 most frequent dim, we choose the one with the
+            # lowest position score (i.e., the leftmost of the most frequent ones)
+            sorted_frequency = sorted(
+                frequency_map.items(),
+                key=lambda x: (-x[1], position_scores[x[0]]),
+            )
+            if len(sorted_frequency) > 1:
+                if not can_dim_value_one_be_dynamic and sorted_frequency[0][0] == 1:
+                    # It's often that dim value one indicates a non-dynamic dimension.
+                    # If the user says so, we pick the second most frequent value.
+                    batch_size = sorted_frequency[1][0]
+                else:
+                    batch_size = sorted_frequency[0][0]
             else:
-                batch_size = sorted_frequency[0][0]
+                if not can_dim_value_one_be_dynamic and sorted_frequency[0][0] == 1:
+                    batch_size = -1
+                else:
+                    batch_size = sorted_frequency[0][0]
         else:
-            if not can_dim_value_one_be_dynamic and sorted_frequency[0][0] == 1:
-                batch_size = -1
-            else:
-                batch_size = sorted_frequency[0][0]
-    else:
-        # no dims to sort: no batch_size
-        batch_size = -1
+            # no dims to sort: no batch_size
+            batch_size = -1
 
     bs_dim = []
     for i in inputs:

fx2ait/fx2ait/lower/lower_settings.py

@@ -68,6 +68,7 @@ class LowerSettings:
     name: str = ""
     dll_name: str = "ait_engine.so"
     dynamic_profile_strategy: DynamicProfileStrategy = DynamicProfileStrategy.MAX
+    dynamic_size: int = -1
     profile_devs: Any = None
     # If None, infer the dtypes from the sample inputs.
     precision: Optional[LowerPrecision] = LowerPrecision.FP16

fx2ait/fx2ait/tensor_spec.py

@@ -477,10 +477,14 @@ def find_batch_size_dim(
         inputs: Any,
         can_non_first_dim_be_dynamic: bool = True,
         can_dim_value_one_be_dynamic: bool = True,
+        dynamic_size: int = -1,
         # pyre-fixme Invalid type [31]
     ) -> []:
         return find_batch_size_dim_impl(
-            inputs, can_non_first_dim_be_dynamic, can_dim_value_one_be_dynamic
+            inputs,
+            can_non_first_dim_be_dynamic,
+            can_dim_value_one_be_dynamic,
+            dynamic_size,
         )
 
     @classmethod