masahi commented on code in PR #14345:
URL: https://github.com/apache/tvm/pull/14345#discussion_r1143913949
##########
python/tvm/topi/hexagon/tensor_intrin.py:
##########
@@ -22,44 +22,164 @@
from tvm import te
+def get_lanes(dtype: str):
+ if "x" not in dtype:
+ return 1
+
+ _, lanes = dtype.split("x")
+ return int(lanes)
+
+
+def is_vector_type(dtype: str):
+ return get_lanes(dtype) != 1
+
+
+def is_power_of_2(n: int):
+ return (n & (n - 1) == 0) and n != 0
+
+
+def _adopt_to_highest_lanes(*args, intrinsic=None, intrinsic_lanes: int = 0):
+ """Enhance original lowering intrinsic to high vector lanes.
+ Will accept vector lanes equal orig_vec_lanes * 2**n for n in [0,1,2...]
+
+ Ada is equivalent of splitting input args to chunk with lanes equal
orig_vec_lanes,
+ execution provided low_intrinsic for each of them and concatenate back.
+
+ Parameters
+ ----------
+ args: List[PrimExpr]
+ Args to adapt to
+
+ intrinsic: callable
+ Intrinsic implementation to adapt
+
+ intrinsic_lanes: int
+ Args lanes supported by provided intrinsic
Review Comment:
Arg lanes?
##########
python/tvm/topi/hexagon/tensor_intrin.py:
##########
@@ -22,44 +22,164 @@
from tvm import te
+def get_lanes(dtype: str):
+ if "x" not in dtype:
+ return 1
+
+ _, lanes = dtype.split("x")
+ return int(lanes)
+
+
+def is_vector_type(dtype: str):
+ return get_lanes(dtype) != 1
+
+
+def is_power_of_2(n: int):
+ return (n & (n - 1) == 0) and n != 0
+
+
+def _adopt_to_highest_lanes(*args, intrinsic=None, intrinsic_lanes: int = 0):
+ """Enhance original lowering intrinsic to high vector lanes.
+ Will accept vector lanes equal orig_vec_lanes * 2**n for n in [0,1,2...]
+
+ Ada is equivalent of splitting input args to chunk with lanes equal
orig_vec_lanes,
+ execution provided low_intrinsic for each of them and concatenate back.
Review Comment:
Ada?
##########
python/tvm/topi/hexagon/tensor_intrin.py:
##########
@@ -22,44 +22,164 @@
from tvm import te
+def get_lanes(dtype: str):
+ if "x" not in dtype:
+ return 1
+
+ _, lanes = dtype.split("x")
+ return int(lanes)
+
+
+def is_vector_type(dtype: str):
+ return get_lanes(dtype) != 1
+
+
+def is_power_of_2(n: int):
+ return (n & (n - 1) == 0) and n != 0
+
+
+def _adopt_to_highest_lanes(*args, intrinsic=None, intrinsic_lanes: int = 0):
+ """Enhance original lowering intrinsic to high vector lanes.
+ Will accept vector lanes equal orig_vec_lanes * 2**n for n in [0,1,2...]
+
+ Ada is equivalent of splitting input args to chunk with lanes equal
orig_vec_lanes,
+ execution provided low_intrinsic for each of them and concatenate back.
Review Comment:
Also please polish this sentence, it is broken.
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