[Metaschedule] Auto tensorization for CPU / GPU dot product (#11088)

* [Metaschedule] Auto-tensorization for CPU / GPU dot product

Co-authored-by: Siyuan Feng <[email protected]>
Co-authored-by: Bohan Hou <[email protected]>
Co-authored-by: Hongyi Jin <[email protected]>
Co-authored-by: Ruihang Lai <[email protected]>
Co-authored-by: Wuwei Lin <[email protected]>

* doc update

* add vnni conv2d test

* add dp4a test

* adding tests for rewrite_tensorize

* add rewrite_tensorize test

* add missing pydoc

* black

* more doc

* adding auto tensorize integration test

* add dp4a test

* fix target name

* fix dtype in test

* skip bert test

* replace hard-coded llvm intrinsic id in test with look up

* remove unnecessary include, add doc for the rest of params

* update postproc.h

* update doc

* fix shape in te matmul workload

* fix newline in cppdoc

Co-authored-by: Siyuan Feng <[email protected]>
Co-authored-by: Bohan Hou <[email protected]>
Co-authored-by: Hongyi Jin <[email protected]>
Co-authored-by: Ruihang Lai <[email protected]>
Co-authored-by: Wuwei Lin <[email protected]>

Author: 6 people

include/tvm/meta_schedule/postproc.h

@@ -149,10 +149,12 @@ class Postproc : public runtime::ObjectRef {
    */
   TVM_DLL static Postproc RewriteUnboundBlock(int max_threadblock);
   /*!
-   * \brief Create a postprocessor that tensorize Tensor Core related components
+   * \brief Create a postprocessor that applies tensorization to annotated blocks
+   * \param vectorize_init_loop Whether or not vectorize the initialization loop produced by
+   * DecomposeReduction
    * \return The postprocessor created.
    */
-  TVM_DLL static Postproc RewriteTensorCore();
+  TVM_DLL static Postproc RewriteTensorize(bool vectorize_init_loop = false);
 
   /*!
    * \brief Creates a postprocessor that verifies if the GPU code is correct

include/tvm/meta_schedule/schedule_rule.h

@@ -150,6 +150,29 @@ class ScheduleRule : public runtime::ObjectRef {
                                                Optional<Array<Integer>> vector_load_lens,    //
                                                Optional<Map<String, ObjectRef>> reuse_read,  //
                                                Optional<Map<String, ObjectRef>> reuse_write);
+
+  /*!
+   * \brief Extension of MultiLevelTiling for auto-tensorizing with a single intrinsic.
+   * \param intrin_name The name of a tensor intrinsic, must be registerd via
+   * TensorIntrin.register(...) beforehand
+   * \param structure The tiling structure. Recommended:
+   * - 'SSRSRS' on CPU
+   * - 'SSSRRSRS' on GPU
+   * \param tile_binds For each level of tiles, which thread axis it is bound to. Recommended:
+   * - NullOpt on CPU
+   * - [blockIdx.x, vthread.x, threadIdx.x] on GPU
+   * \param max_innermost_factor The maximum size of the innermost factor. NullOpt means no limit
+   * \param vector_load_lens The length of vector lane in vectorized cooperative fetching.
+   * NullOpt means disable vectorization
+   * \param reuse_read Data reuse configuration for reading. NullOpt means no reuse.
+   * \param reuse_write Data reuse configuration for writing. NullOpt means no reuse.
+   * \return The schedule rule created
+   */
+  TVM_DLL static ScheduleRule MultiLevelTilingWithIntrin(
+      String intrin_name, String structure, Optional<Array<String>> tile_binds,
+      Optional<Integer> max_innermost_factor, Optional<Array<Integer>> vector_load_lens,
+      Optional<Map<String, ObjectRef>> reuse_read, Optional<Map<String, ObjectRef>> reuse_write);
+
   /*!
    * \brief Create a rule: add-rfactor to some blocks if needed
    * \param max_jobs_per_core The maximum number of jobs to be launched per CPU core. It sets the

include/tvm/tir/stmt.h

@@ -1509,6 +1509,11 @@ constexpr const char* meta_schedule_unroll_explicit = "meta_schedule.unroll_expl
 /*! \brief Mark auto-unroll setting on the block. */
 constexpr const char* meta_schedule_unroll_implicit = "meta_schedule.unroll_implicit";
 
+/*!
+ * \brief Mark that a block should be further rewritten using tensorization.
+ */
+constexpr const char* meta_schedule_auto_tensorize = "meta_schedule.auto_tensorize";
+
 /*!
  * \brief Check if attr_key is a pragma key extension
  * \param attr_key The attr key to be compared

python/tvm/meta_schedule/postproc/__init__.py

@@ -22,3 +22,4 @@
 from .rewrite_reduction_block import RewriteReductionBlock
 from .rewrite_unbound_block import RewriteUnboundBlock
 from .verify_gpu_code import VerifyGPUCode
+from .rewrite_tensorize import RewriteTensorize

python/tvm/meta_schedule/postproc/rewrite_tensorize.py

@@ -0,0 +1,38 @@
+# 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.
+"""A postprocessor that tensorize related components."""
+
+from tvm._ffi.registry import register_object
+from .. import _ffi_api
+from .postproc import Postproc
+
+
+@register_object("meta_schedule.RewriteTensorize")
+class RewriteTensorize(Postproc):
+    """A postprocessor that applies tensorization to annotated blocks.
+
+    Parameters
+    ----------
+    vectorize_init_loop : bool
+       Whether or not vectorize the initialization loop produced by DecomposeReduction
+    """
+
+    def __init__(self, vectorize_init_loop=False) -> None:
+        self.__init_handle_by_constructor__(
+            _ffi_api.PostprocRewriteTensorize,  # type: ignore # pylint: disable=no-member
+            vectorize_init_loop,
+        )

python/tvm/meta_schedule/schedule_rule/__init__.py

@@ -22,7 +22,7 @@
 from .add_rfactor import AddRFactor
 from .auto_inline import AutoInline
 from .cross_thread_reduction import CrossThreadReduction
-from .multi_level_tiling import MultiLevelTiling, ReuseType
+from .multi_level_tiling import MultiLevelTiling, MultiLevelTilingWithIntrin, ReuseType
 from .parallel_vectorize_unroll import ParallelizeVectorizeUnroll
 from .random_compute_location import RandomComputeLocation
 from .schedule_rule import PyScheduleRule, ScheduleRule

python/tvm/meta_schedule/schedule_rule/multi_level_tiling.py

@@ -82,3 +82,52 @@ def __init__(
             reuse_read.as_dict() if reuse_read is not None else None,
             reuse_write.as_dict() if reuse_write is not None else None,
         )
+
+
+@register_object("meta_schedule.MultiLevelTilingWithIntrin")
+class MultiLevelTilingWithIntrin(ScheduleRule):
+    """Extension of MultiLevelTiling for auto-tensorizing with a single intrinsic.
+
+    Parameters
+    ----------
+    intrin_name : str
+        The name of a tensor intrinsic, must be registerd via TensorIntrin.register(...) beforehand
+    structure : str
+        The tiling structure. Recommended:
+        - 'SSRSRS' on CPU
+        - 'SSSRRSRS' on GPU
+    tile_bind : Optional[List[str]]
+        For each level of tiles, which thread axis it is bound to. Recommended:
+        - None on CPU
+        - [blockIdx.x, vthread.x, threadIdx.x] on GPU
+    max_innermost_factor : Optional[int]
+        The maximum size of the innermost factor. None means no limit
+    vector_load_lens : Optional[List[int]]
+        The length of vector lane in vectorized cooperative fetching.
+        None means disable vectorization
+    reuse_read : Optional[ReuseType]
+        Data reuse configuration for reading. None means no reuse.
+    reuse_write : Optional[ReuseType]
+        Data reuse configuration for writing. None means no reuse.
+    """
+
+    def __init__(
+        self,
+        intrin_name: str,
+        structure: str,
+        tile_binds: Optional[List[str]] = None,
+        max_innermost_factor: Optional[int] = None,
+        vector_load_lens: Optional[List[int]] = None,
+        reuse_read: Optional[ReuseType] = None,
+        reuse_write: Optional[ReuseType] = None,
+    ) -> None:
+        self.__init_handle_by_constructor__(
+            _ffi_api.ScheduleRuleMultiLevelTilingWithIntrin,  # type: ignore # pylint: disable=no-member
+            intrin_name,
+            structure,
+            tile_binds,
+            max_innermost_factor,
+            vector_load_lens,
+            reuse_read.as_dict() if reuse_read is not None else None,
+            reuse_write.as_dict() if reuse_write is not None else None,
+        )

python/tvm/meta_schedule/testing/te_workload.py

@@ -607,7 +607,7 @@ def f_compute(i, j):
 
 def matmul_relu(n: int, m: int, k: int) -> Tuple[te.Tensor, te.Tensor, te.Tensor]:
     a = te.placeholder((n, k), name="A")
-    b = te.placeholder((m, k), name="B")
+    b = te.placeholder((k, m), name="B")
     k = te.reduce_axis((0, k), name="k")
     c = te.compute(
         (n, m),

src/meta_schedule/postproc/rewrite_tensorize.cc

@@ -0,0 +1,105 @@
+/*
+ * 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 <tvm/meta_schedule/postproc.h>
+
+#include <algorithm>
+
+#include "../utils.h"
+
+namespace tvm {
+namespace meta_schedule {
+
+using tir::BlockRV;
+using tir::LoopRV;
+
+void ApplyTensorization(const tir::Schedule& sch, const String& func_name,
+                        const tir::PrimFuncNode* func, bool vectorize_init_loop) {
+  std::vector<std::pair<std::string, std::function<void(tir::BlockRV)>>> jobs;
+
+  tir::PostOrderVisit(func->body, [=, &jobs](const ObjectRef& obj) {
+    if (const auto* block = obj.as<tir::BlockNode>()) {
+      tir::StmtSRef block_sref = sch->GetSRef(block);
+      if (Optional<String> intrin_name =
+              tir::GetAnn<String>(block_sref, tir::attr::meta_schedule_auto_tensorize)) {
+        std::string block_name = block_sref->StmtAs<tir::BlockNode>()->name_hint;
+        if (block_name.find("init") == std::string::npos) {
+          jobs.emplace_back(block_name, [sch, intrin_name](tir::BlockRV block) {
+            try {
+              sch->Tensorize(block, intrin_name.value());
+            } catch (const std::exception& e) {
+              LOG(WARNING) << "Tensorize failed with error " << e.what();
+            }
+          });
+        } else if (vectorize_init_loop) {
+          jobs.emplace_back(block_name, [sch](tir::BlockRV block) {
+            Array<BlockRV> child_blocks = sch->GetChildBlocks(block);
+            ICHECK(child_blocks.size() == 1);
+            Array<LoopRV> init_loops = sch->GetLoops(child_blocks[0]);
+            ICHECK(init_loops.size() == 1);
+            sch->Vectorize(init_loops[0]);
+          });
+        }
+      }
+    }
+  });
+
+  for (auto kv : jobs) {
+    tir::BlockRV block = sch->GetBlock(kv.first, func_name);
+    sch->Unannotate(block, tir::attr::meta_schedule_auto_tensorize);
+    kv.second(block);
+  }
+}
+
+class RewriteTensorizeNode : public PostprocNode {
+ public:
+  void InitializeWithTuneContext(const TuneContext& context) final {}
+
+  bool Apply(const tir::Schedule& sch) final;
+
+  void VisitAttrs(tvm::AttrVisitor* v) {}
+
+  bool vectorize_init_loop = false;
+
+  static constexpr const char* _type_key = "meta_schedule.RewriteTensorize";
+  TVM_DECLARE_FINAL_OBJECT_INFO(RewriteTensorizeNode, PostprocNode);
+};
+
+bool RewriteTensorizeNode::Apply(const tir::Schedule& sch) {
+  for (const auto& kv : sch->mod()->functions) {
+    GlobalVar g_var = kv.first;
+    BaseFunc base_func = kv.second;
+    if (const tir::PrimFuncNode* prim_func = base_func.as<tir::PrimFuncNode>()) {
+      ApplyTensorization(sch, g_var->name_hint, prim_func, vectorize_init_loop);
+    }
+  }
+  return true;
+}
+
+Postproc Postproc::RewriteTensorize(bool vectorize_init_loop) {
+  ObjectPtr<RewriteTensorizeNode> n = make_object<RewriteTensorizeNode>();
+  n->vectorize_init_loop = vectorize_init_loop;
+  return Postproc(n);
+}
+
+TVM_REGISTER_NODE_TYPE(RewriteTensorizeNode);
+TVM_REGISTER_GLOBAL("meta_schedule.PostprocRewriteTensorize")
+    .set_body_typed(Postproc::RewriteTensorize);
+
+}  // namespace meta_schedule
+}  // namespace tvm

src/meta_schedule/schedule_rule/multi_level_tiling.cc

@@ -260,28 +260,9 @@ ScheduleRule ScheduleRule::MultiLevelTiling(String structure, Optional<Array<Str
                                             Optional<Array<Integer>> vector_load_lens,
                                             Optional<Map<String, ObjectRef>> reuse_read,
                                             Optional<Map<String, ObjectRef>> reuse_write) {
-  ObjectPtr<MultiLevelTilingNode> n = make_object<MultiLevelTilingNode>();
-  n->structure = structure;
-  n->tile_binds = tile_binds.value_or({});
-  n->max_innermost_factor = max_innermost_factor.value_or(Integer(-1))->value;
-  n->vector_load_lens = vector_load_lens.defined()
-                            ? support::AsVector<Integer, int>(vector_load_lens.value())
-                            : std::vector<int>();
-  n->reuse_read_ = reuse_read.defined() ? ReuseConfig(reuse_read.value()) : ReuseConfig();
-  n->reuse_write_ = reuse_write.defined() ? ReuseConfig(reuse_write.value()) : ReuseConfig();
-  for (int i = 0, len = structure.size(); i < len; ++i) {
-    char c = structure.data()[i];
-    if (c == 'S') {
-      n->s_indices_.push_back(i);
-    } else if (c == 'R') {
-      n->r_indices_.push_back(i);
-    } else {
-      LOG(FATAL) << "ValueError: Invalid tiling structure: " << structure;
-    }
-  }
-  n->thread_warp_size_ = -1;
-  n->max_threads_per_block_ = -1;
-  return ScheduleRule(n);
+  auto node = MultiLevelTilingInitCommon<MultiLevelTilingNode>(
+      structure, tile_binds, max_innermost_factor, vector_load_lens, reuse_read, reuse_write);
+  return ScheduleRule(node);
 }
 
 TVM_REGISTER_NODE_TYPE(MultiLevelTilingNode);