chore: import upstream snapshot with attribution

python/tvm/s_tir/dlight/gpu/rmsnorm.py

@@ -0,0 +1,143 @@
+# 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.
+# pylint: disable=missing-docstring
+"""A RMS norm schedule rule for GPU operators."""
+
+import tvm
+from tvm import tirx
+from tvm.ir import Call
+from tvm.target import Target
+from tvm.tirx import BufferStore, SBlock
+from tvm.tirx.expr import BufferLoad, Cast
+
+from ..base import ScheduleRule
+
+
+def identify_cast_or_load_block(block: SBlock) -> bool:
+    if len(block.reads) != 1 or len(block.writes) != 1:
+        return False
+
+    if not isinstance(block.body, BufferStore):
+        return False
+    store = block.body
+
+    # check types
+    if isinstance(store.value, BufferLoad):
+        load = store.value
+    elif isinstance(store.value, Cast):
+        load = store.value.value
+        if not isinstance(load, BufferLoad):
+            return False
+    else:
+        return False
+
+    # check indices
+    if len(load.indices) != len(store.indices):
+        return False
+
+    for lhs, rhs in zip(load.indices, store.indices):
+        if not lhs.same_as(rhs):
+            return False
+
+    return True
+
+
+def identify_rsqrt_block(block: SBlock) -> bool:
+    if len(block.reads) != 1 or len(block.writes) != 1:
+        return False
+
+    if not isinstance(block.body, BufferStore):
+        return False
+    store = block.body
+
+    if not isinstance(store.value, Call):
+        return False
+    call = store.value
+    op = call.op
+
+    return op == tvm.ir.op.Op.get("tirx.rsqrt")
+
+
+class RMSNorm(ScheduleRule):
+    """A rule for RMS norm."""
+
+    def apply(  # pylint: disable=too-many-locals,missing-docstring
+        self,
+        func: tirx.PrimFunc,
+        target: Target,
+        _: bool,
+    ) -> "tvm.s_tir.Schedule":
+        if target.kind.name == "cuda":
+            num_tx = 512
+        elif target.kind.name == "opencl":
+            num_tx = 256
+        else:
+            num_tx = 64
+
+        sch = tvm.s_tir.Schedule(func)
+        root = sch.get_sblock(name="root", func_name="main")
+
+        blocks = sch.get_child_blocks(root)
+
+        if not any([identify_rsqrt_block(sch.get(block)) for block in blocks]):
+            return None
+
+        read = sch.cache_read(block=blocks[0], read_buffer_index=0, storage_scope="local")
+        write = sch.cache_write(block=blocks[-1], write_buffer_index=0, storage_scope="local")
+
+        for block in blocks:
+            if identify_cast_or_load_block(sch.get(block)):
+                sch.compute_inline(block)
+
+        blocks = sch.get_child_blocks(root)
+
+        read, sqr, redsum, rsqrt, norm, write = blocks
+
+        if not identify_rsqrt_block(sch.get(rsqrt)):
+            return None
+
+        for name in [read, sqr, redsum, rsqrt, norm, write]:
+            loops = sch.get_loops(name)
+            sch.fuse(*loops[:-1])
+
+        block_loop, loops = sch.get_loops(block=read)
+        thread_loop, _, _ = sch.split(
+            loop=loops, factors=[num_tx, None, 8], preserve_unit_iters=True
+        )
+        sch.bind(block_loop, thread_axis="blockIdx.x")
+        sch.bind(thread_loop, thread_axis="threadIdx.x")
+        sch.vectorize(sch.get_loops(block=read)[-1])
+        sch.reverse_compute_at(block=sqr, loop=thread_loop)
+        sch.reverse_compute_at(block=redsum, loop=thread_loop)
+
+        sch.reverse_compute_at(block=rsqrt, loop=block_loop, index=-1)
+        sch.reverse_compute_at(block=norm, loop=block_loop, index=-1)
+        block_loop, loops = sch.get_loops(block=norm)
+        thread_loop, _, _ = sch.split(
+            loop=loops, factors=[num_tx, None, 8], preserve_unit_iters=True
+        )
+        sch.bind(thread_loop, thread_axis="threadIdx.x")
+
+        sch.reverse_compute_at(block=write, loop=thread_loop, index=-1)
+        sch.vectorize(sch.get_loops(block=write)[-1])
+
+        sch.set_scope(block=sqr, buffer_index=0, storage_scope="local")
+        sch.set_scope(block=redsum, buffer_index=0, storage_scope="local")
+        sch.set_scope(block=rsqrt, buffer_index=0, storage_scope="shared")
+        sch.set_scope(block=norm, buffer_index=0, storage_scope="local")
+
+        return sch