[MetaSchedule] Schedule Rule: Auto Inline

Co-authored-by: Junru Shao <junrushao1994@gmail.com>
Co-authored-by: Xiyou Zhou <xiyou@octoml.ai>
Co-authored-by: Bohan Hou <32121147+spectrometerHBH@users.noreply.github.com>
Co-authored-by: Siyuan Feng <Hzfengsy@sjtu.edu.cn>
Co-authored-by: Hongyi Jin <3231950289@qq.com>
Co-authored-by: Wuwei Lin <wuwei@apache.org>

include/tvm/meta_schedule/schedule_rule.h

@@ -115,7 +115,6 @@ class ScheduleRule : public runtime::ObjectRef {
    * \brief Create an auto-inline rule that inlines spatial blocks if it satisfies some conditions
    * \param into_producer If allows to inline a block into its producer
    * \param into_consumer If allows to inline a block into its consumer
-   * \param into_cache_only If it only allows to inline into a block generated by cache_read/write
    * \param inline_const_tensor Always inline constant tensors
    * \param disallow_if_then_else Always disallow if-then-else-like constructs
    * \param require_ordered Always require the read-to-write mapping to be ordered
@@ -125,7 +124,6 @@ class ScheduleRule : public runtime::ObjectRef {
    */
   TVM_DLL static ScheduleRule AutoInline(bool into_producer,          //
                                          bool into_consumer,          //
-                                         bool into_cache_only,        //
                                          bool inline_const_tensor,    //
                                          bool disallow_if_then_else,  //
                                          bool require_injective,      //

python/tvm/meta_schedule/schedule_rule/__init__.py

@@ -16,4 +16,5 @@
 Meta Schedule schedule rules are used for modification of
 blocks in a schedule. See also PostOrderApply.
 """
+from .auto_inline import AutoInline
 from .schedule_rule import PyScheduleRule, ScheduleRule

python/tvm/meta_schedule/schedule_rule/auto_inline.py

@@ -0,0 +1,67 @@
+# 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.
+"""Auto-Inline. Rule that inlines spatial blocks if it satisfies some conditions"""
+from typing import List, Optional
+
+from tvm._ffi import register_object
+
+from .. import _ffi_api
+from .schedule_rule import ScheduleRule
+
+
+@register_object("meta_schedule.AutoInline")
+class AutoInline(ScheduleRule):
+    """Rule that inlines spatial blocks if it satisfies some conditions
+
+    Parameters
+    ----------
+    into_producer : bool
+        If allows to inline a block into its producer
+    into_consumer : bool
+        If allows to inline a block into its consumer
+    inline_const_tensor : bool
+        Always inline constant tensors
+    disallow_if_then_else : bool
+        Always disallow if-then-else-like constructs
+    require_injective : bool
+        Always require the read-to-write mapping to be ordered
+    require_ordered : bool
+        Always require the read-to-write mapping to be injective
+    disallow_op : Optional[List[str]]
+        The operators that are disallowed in auto inline
+    """
+
+    def __init__(
+        self,
+        into_producer: bool,
+        into_consumer: bool,
+        inline_const_tensor: bool,
+        disallow_if_then_else: bool,
+        require_injective: bool,
+        require_ordered: bool,
+        disallow_op: Optional[List[str]] = None,
+    ) -> None:
+        self.__init_handle_by_constructor__(
+            _ffi_api.ScheduleRuleAutoInline,  # type: ignore # pylint: disable=no-member
+            into_producer,
+            into_consumer,
+            inline_const_tensor,
+            disallow_if_then_else,
+            require_injective,
+            require_ordered,
+            disallow_op,
+        )

python/tvm/meta_schedule/testing/schedule_rule.py

@@ -0,0 +1,47 @@
+# 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.
+"""Default schedule rules"""
+from tvm.meta_schedule.schedule_rule import (
+    AutoInline,
+    ScheduleRule,
+)
+from tvm.target import Target
+
+
+def auto_inline(target: Target) -> ScheduleRule:
+    """Default schedule rules for auto inline"""
+    if target.kind.name == "llvm":
+        return AutoInline(
+            into_producer=False,
+            into_consumer=True,
+            inline_const_tensor=True,
+            disallow_if_then_else=True,
+            require_injective=True,
+            require_ordered=True,
+            disallow_op=["tir.exp"],
+        )
+    if target.kind.name == "cuda":
+        return AutoInline(
+            into_producer=True,
+            into_consumer=True,
+            inline_const_tensor=True,
+            disallow_if_then_else=False,
+            require_injective=False,
+            require_ordered=False,
+            disallow_op=None,
+        )
+    raise NotImplementedError(f"{target.kind.name} is not supported")

src/meta_schedule/schedule_rule/auto_inline.cc

@@ -0,0 +1,174 @@
+/*
+ * 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.
+ */
+#include "../utils.h"
+
+namespace tvm {
+namespace meta_schedule {
+
+/*! \brief The type of inline to be performed on a specific block */
+enum class InlineType : int32_t {
+  /*! \brief No inline opportunity */
+  kNoInline = 0,
+  /*! \brief Inline the block into its consumer */
+  kInlineIntoConsumer = 1,
+  /*! \brief Inline the block into its producer */
+  kInlineIntoProducer = 2,
+};
+
+/*! \brief The rule that inlines spatial blocks if it satisfies some conditions. */
+class AutoInlineNode : public ScheduleRuleNode {
+ public:
+  /*! \brief Checks if the specific block should be inlined */
+  inline InlineType CheckInline(const tir::Schedule& sch, const tir::BlockRV& block_rv);
+
+  // Inherited from ScheduleRuleNode
+  void InitializeWithTuneContext(const TuneContext& context) final {}
+
+  // Inherited from ScheduleRuleNode
+  Array<tir::Schedule> Apply(const tir::Schedule& sch, const tir::BlockRV& block_rv) final {
+    InlineType inline_type = CheckInline(sch, block_rv);
+    if (inline_type == InlineType::kInlineIntoConsumer) {
+      sch->ComputeInline(block_rv);
+    } else if (inline_type == InlineType::kInlineIntoProducer) {
+      sch->ReverseComputeInline(block_rv);
+    }
+    return {sch};
+  }
+
+ public:
+  /*! \brief If allows to inline a block into its producer */
+  bool into_producer;
+  /*! \brief If allows to inline a block into its consumer */
+  bool into_consumer;
+  /*! \brief Always inline constant tensors */
+  bool inline_const_tensor;
+  /*! \brief Always disallow if-then-else-like constructs */
+  bool disallow_if_then_else;
+  /*! \brief Always require the read-to-write mapping to be injective to do auto inline */
+  bool require_injective;
+  /*! \brief Always require the read-to-write mapping to be ordered to do auto inline */
+  bool require_ordered;
+  /*! \brief The operators that are disallowed in auto inline */
+  Array<Op> disallow_op;
+
+  void VisitAttrs(tvm::AttrVisitor* v) {
+    v->Visit("into_producer", &into_producer);
+    v->Visit("into_consumer", &into_consumer);
+    v->Visit("inline_const_tensor", &inline_const_tensor);
+    v->Visit("disallow_if_then_else", &disallow_if_then_else);
+    v->Visit("require_injective", &require_injective);
+    v->Visit("require_ordered", &require_ordered);
+    v->Visit("disallow_op", &disallow_op);
+  }
+
+  static constexpr const char* _type_key = "meta_schedule.AutoInline";
+  TVM_DECLARE_FINAL_OBJECT_INFO(AutoInlineNode, ScheduleRuleNode);
+};
+
+inline InlineType AutoInlineNode::CheckInline(const tir::Schedule& sch,
+                                              const tir::BlockRV& block_rv) {
+  using namespace tvm::tir;
+  StmtSRef block_sref = sch->GetSRef(block_rv);
+  ScheduleState state = sch->state();
+  const BlockNode* block = TVM_SREF_TO_BLOCK(block, block_sref);
+  BlockRealize realize = GetBlockRealize(state, block_sref);
+  // Cond 1. The block has only one write buffer
+  if (block->writes.size() != 1) {
+    return InlineType::kNoInline;
+  }
+  // Cond 2. For a block that generates a constant tensor, ignore all other conditions
+  if (inline_const_tensor && block->reads.empty()) {
+    return InlineType::kInlineIntoConsumer;
+  }
+  // Cond 3. The block doesn't contain any disallowed operators
+  if (!disallow_op.empty() && HasOp(realize, disallow_op)) {
+    return InlineType::kNoInline;
+  }
+  // Cond 4. The block doesn't have any if-then-else-like constructs
+  if (disallow_if_then_else && HasIfThenElse(realize)) {
+    return InlineType::kNoInline;
+  }
+  // Cond 5. The mapping from read indices to write indices are injective and ordered
+  if (require_injective || require_ordered) {
+    const BufferRegion& write_region = block->writes[0];
+    for (const BufferRegion& read_region : block->reads) {
+      bool injective, ordered;
+      auto _ = std::ignore;
+      std::tie(/*exists=*/_, /*surjective=*/_, injective, ordered, /*no_const_read=*/_,
+               /*no_shift_read=*/_) = AnalyzeReadWritePattern(read_region, write_region);
+      if (require_injective && injective == false) {
+        return InlineType::kNoInline;
+      }
+      if (require_ordered && ordered == false) {
+        return InlineType::kNoInline;
+      }
+    }
+  }
+  // Last cond: Check inline into the consumers or the spatial producer
+  tir::StmtSRef scope_block = tir::GetScopeRoot(sch->state(), block_sref,          //
+                                                /*require_stage_pipeline=*/false,  //
+                                                /*require_subtree_compact_dataflow=*/false);
+  if (into_consumer) {
+    Array<tir::StmtSRef> consumer_srefs = GetConsumers(state, block_sref);
+    if (!consumer_srefs.empty() && CanComputeInline(state, block_sref)) {
+      return InlineType::kInlineIntoConsumer;
+    }
+  }
+  if (into_producer) {
+    Array<tir::StmtSRef> producer_srefs = GetProducers(state, block_sref);
+    if (producer_srefs.size() == 1 &&
+        tir::IsCompleteBlock(sch->state(), producer_srefs[0], scope_block) &&
+        CanReverseComputeInline(state, block_sref)) {
+      return InlineType::kInlineIntoProducer;
+    }
+  }
+  return InlineType::kNoInline;
+}
+
+ScheduleRule ScheduleRule::AutoInline(bool into_producer,          //
+                                      bool into_consumer,          //
+                                      bool inline_const_tensor,    //
+                                      bool disallow_if_then_else,  //
+                                      bool require_injective,      //
+                                      bool require_ordered,        //
+                                      Optional<Array<String>> disallow_op) {
+  ObjectPtr<AutoInlineNode> n = make_object<AutoInlineNode>();
+  n->into_producer = into_producer;
+  n->into_consumer = into_consumer;
+  n->inline_const_tensor = inline_const_tensor;
+  n->disallow_if_then_else = disallow_if_then_else;
+  n->require_injective = require_injective;
+  n->require_ordered = require_ordered;
+  n->disallow_op.clear();
+  if (disallow_op.defined()) {
+    Array<String> op_names = disallow_op.value();
+    n->disallow_op.reserve(op_names.size());
+    for (const String& op_name : op_names) {
+      n->disallow_op.push_back(Op::Get(op_name));
+    }
+  }
+  return ScheduleRule(n);
+}
+
+TVM_REGISTER_NODE_TYPE(AutoInlineNode);
+TVM_REGISTER_GLOBAL("meta_schedule.ScheduleRuleAutoInline")
+    .set_body_typed(ScheduleRule::AutoInline);
+
+}  // namespace meta_schedule
+}  // namespace tvm

src/tir/schedule/analysis.h

@@ -20,6 +20,7 @@
 #define TVM_TIR_SCHEDULE_ANALYSIS_H_
 
 #include <tvm/arith/analyzer.h>
+#include <tvm/ir/op.h>
 #include <tvm/tir/schedule/state.h>
 
 #include <tuple>
@@ -442,6 +443,50 @@ bool CanComputeAt(const ScheduleState& self, const StmtSRef& block_sref, const S
 bool CanReverseComputeAt(const ScheduleState& self, const StmtSRef& block_sref,
                          const StmtSRef& loop_sref, bool preserve_unit_loops);
 
+/*!
+ * \brief Checks if the given AST contains the specific operators
+ * \param stmt The AST statement to be checked
+ * \param ops The list of operators to be checked
+ * \return A boolean indicating whether the AST contains the specific operators
+ */
+bool HasOp(const Stmt& stmt, const Array<Op>& ops);
+
+/*!
+ * \brief Checks if the given AST statement contains if-then-else, including
+ * 1) IfThenElse statement
+ * 2) Select expression
+ * 3) The operator `tir.if_then_else`
+ * 4) non-constant-true Block predicates
+ * \param stmt The AST statement to be checked
+ * \return A boolean indicating whether the statement contains the if-then-else pattern
+ */
+bool HasIfThenElse(const Stmt& stmt);
+
+/*!
+ * \brief Given the read/write region, extract the pattern of their index correspondence
+ * namely, the mapping from read index to the write index.
+ * \param read_region The read region
+ * \param write_region The write region
+ * \return A tuple of booleans, the extracted pattern
+ * 0) exists: if the pattern is found
+ * 1) surjective: if the pattern is surjective, i.e. each write index is mapped at least once
+ *    e.g. A[i, j] = B[i, i, j]
+ * 2) injective: if the pattern is injective, i.e. each write index is mapped at most once.
+ *    e.g. A[i, j] = B[i]
+ * 3) ordered: if the mapping is ordered
+ * 4) no_const_read: if there is no constant indexing in the read indices,
+ *    e.g. A[i, j] = B[0, i, j]
+ * 5) no_shift_read: if there is no constant shift in the read indices,
+ *    e.g. A[i, j] = B[i + 1, j]
+ */
+std::tuple</*exists=*/bool,
+           /*surjective=*/bool,
+           /*injective=*/bool,
+           /*ordered=*/bool,
+           /*no_const_read=*/bool,
+           /*no_shift_read=*/bool>
+AnalyzeReadWritePattern(const BufferRegion& read_region, const BufferRegion& write_region);
+
 }  // namespace tir
 }  // namespace tvm