[GitHub] [tvm] nverke commented on a diff in pull request #12654: [Hexagon] Create test examples to show parallelization

[GitBox <gi...@apache.org>]

nverke commented on code in PR #12654:
URL: https://github.com/apache/tvm/pull/12654#discussion_r968656484


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tests/python/contrib/test_hexagon/test_parallel_scalar.py:
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@@ -0,0 +1,178 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+""" Test parallelism for multiple different scalar workloads. """
+
+import numpy as np
+import tvm
+
+from tvm.script import tir as T
+from numpy.random import default_rng
+
+TEST_OUTPUT_TEMPLATE = "Test {} with {} operations... \n    -Single Thread: {} ms \n    -Parallel: {} ms\n    -Speedup: {}x\n"
+
+
+def get_add_operator(operations):
+    @T.prim_func
+    def operator(a: T.handle, b: T.handle, c: T.handle) -> None:
+        T.func_attr({"global_symbol": "main", "tir.noalias": True})
+        A = T.match_buffer(a, [operations], dtype="float64")
+        B = T.match_buffer(b, [operations], dtype="float64")
+        C = T.match_buffer(c, [operations], dtype="float64")
+        for n in T.grid(operations):
+            with T.block("C"):
+                vn = T.axis.remap("S", [n])
+                C[vn] = A[vn] + B[vn]
+
+    return operator
+
+
+def get_multiply_operator(operations):
+    @T.prim_func
+    def operator(a: T.handle, b: T.handle, c: T.handle) -> None:
+        T.func_attr({"global_symbol": "main", "tir.noalias": True})
+        A = T.match_buffer(a, [operations], dtype="float64")
+        B = T.match_buffer(b, [operations], dtype="float64")
+        C = T.match_buffer(c, [operations], dtype="float64")
+        for n in T.grid(operations):
+            with T.block("C"):
+                vn = T.axis.remap("S", [n])
+                C[vn] = A[vn] * B[vn]
+
+    return operator
+
+
+def get_sub_operator(operations):
+    @T.prim_func
+    def operator(a: T.handle, b: T.handle, c: T.handle) -> None:
+        T.func_attr({"global_symbol": "main", "tir.noalias": True})
+        A = T.match_buffer(a, [operations], dtype="float64")
+        B = T.match_buffer(b, [operations], dtype="float64")
+        C = T.match_buffer(c, [operations], dtype="float64")
+        for n in T.grid(operations):
+            with T.block("C"):
+                vn = T.axis.remap("S", [n])
+                C[vn] = A[vn] - B[vn]
+
+    return operator
+
+
+def evaluate(hexagon_session, operations, expected, sch):
+    shape = operations
+    dtype = "float64"

Review Comment:
   Yeah, thats the idea, I know that hvx does not support float64 



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