[GitHub] [tvm] Lunderberg commented on a diff in pull request #11449: [Hexagon] Slice op relu

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

Lunderberg commented on code in PR #11449:
URL: https://github.com/apache/tvm/pull/11449#discussion_r882140311


##########
python/tvm/topi/hexagon/slice_ops/relu.py:
##########
@@ -0,0 +1,56 @@
+# 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.
+
+import tvm
+from tvm import te, tir
+from tvm.ir.module import IRModule
+from tvm.script import tir as T
+
+
+def relu_te_compute(Input, out_shape, dtype):
+    x = tvm.tir.const(0, dtype)
+    Output = te.compute(
+        out_shape, lambda n, h, w, c: tvm.te.max(Input[n, h, w, c], x), name="reluf16"

Review Comment:
   Instead of using `out_shape` as an argument to `te.compute`, I'd recommend using `Input.shape`.  That way, the `out_shape` parameter could be removed, and the user wouldn't need to specify it independent of the `Input`.



##########
python/tvm/topi/hexagon/slice_ops/relu.py:
##########
@@ -0,0 +1,56 @@
+# 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.
+
+import tvm
+from tvm import te, tir
+from tvm.ir.module import IRModule
+from tvm.script import tir as T
+
+
+def relu_te_compute(Input, out_shape, dtype):
+    x = tvm.tir.const(0, dtype)
+    Output = te.compute(
+        out_shape, lambda n, h, w, c: tvm.te.max(Input[n, h, w, c], x), name="reluf16"
+    )
+    return Output
+
+
+def reluf16_te_sched(Output, Input, transform_crouton_activation):
+    s = tvm.te.create_schedule(Output.op)
+    s[Input].transform_layout(transform_crouton_activation)
+    out_axes = s[Output].transform_layout(transform_crouton_activation)
+    fused = s[Output].fuse(out_axes[6], out_axes[7])
+    s[Output].vectorize(fused)
+    return s
+
+
+def reluf16_stir_sched(func, transform_crouton_activation):
+    sch = tir.Schedule(func, debug_mask="all")
+    block = sch.get_block("reluf16")
+    n, i, j, k = sch.get_loops(block)
+    i1, i2 = sch.split(i, [None, 8])
+    j1, j2 = sch.split(j, [None, 4])
+    k1, k2 = sch.split(k, [None, 32])
+    j3, j4 = sch.split(j2, [None, 2])
+    sch.reorder(n, i1, j1, k1, i2, j3, k2, j4)
+    sch.transform_layout(block, 0, "read", transform_crouton_activation)
+    sch.set_axis_separator(block, 0, "read", [4])

Review Comment:
   FYI, after https://github.com/apache/tvm/pull/11269 lands, the `set_axis_separator` will be made automatically based on `IndexMap.AXIS_SEPARATOR`, similar to how it is handled in TE-based schedules.



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