[Meta Schedule] Per-Store-Feature

include/tvm/meta_schedule/feature_extractor.h

@@ -91,14 +91,26 @@ class PyFeatureExtractorNode : public FeatureExtractorNode {
  */
 class FeatureExtractor : public runtime::ObjectRef {
  public:
+  /*!
+   * \brief Create a feature extractor that extracts features from each BufferStore
+   * \param buffers_per_store The number of buffers in each BufferStore; Pad or truncate if
+   * necessary.
+   * \param arith_intensity_curve_num_samples The number of samples used in the arithmetic intensity
+   * curve.
+   * \param cache_line_bytes The number of bytes in a cache line.
+   * \return The feature extractor created.
+   */
+  TVM_DLL static FeatureExtractor PerStoreFeature(int buffers_per_store = 5,
+                                                  int arith_intensity_curve_num_samples = 10,
+                                                  int cache_line_bytes = 64);
   /*!
    * \brief Create a feature extractor with customized methods on the python-side.
    * \param f_extract_from The packed function of `ExtractFrom`.
    * \param f_as_string The packed function of `AsString`.
    * \return The feature extractor created.
    */
   TVM_DLL static FeatureExtractor PyFeatureExtractor(
-      PyFeatureExtractorNode::FExtractFrom f_extract_from,  //
+      PyFeatureExtractorNode::FExtractFrom f_extract_from,
       PyFeatureExtractorNode::FAsString f_as_string);
   TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(FeatureExtractor, ObjectRef, FeatureExtractorNode);
 };

python/tvm/meta_schedule/__init__.py

@@ -28,3 +28,4 @@
 from . import feature_extractor
 from . import cost_model
 from .tune_context import TuneContext
+from .search_strategy import MeasureCandidate

python/tvm/meta_schedule/feature_extractor/__init__.py

@@ -20,3 +20,4 @@
 measure candidates for use in cost model.
 """
 from .feature_extractor import FeatureExtractor, PyFeatureExtractor
+from .per_store_feature import PerStoreFeature

python/tvm/meta_schedule/feature_extractor/per_store_feature.py

@@ -0,0 +1,71 @@
+# 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.
+"""We extract one feature vector per BufferStoreNode statement in a TIR Stmt,
+so we call this feature as "per-store" feature.
+"""
+from tvm._ffi import register_object
+
+from .. import _ffi_api
+from .feature_extractor import FeatureExtractor
+
+
+# /*!
+#  * \brief Create a feature extractor that extracts features from each BufferStore
+#  * \param buffers_per_store The number of buffers in each BufferStore; Pad or truncate if
+#  * necessary.
+#  * \param arith_intensity_curve_num_samples The number of samples used in the arithmetic intensity
+#  * curve.
+#  * \param cache_line_bytes The number of bytes in a cache line.
+#  * \return The feature extractor created.
+#  */
+
+
+@register_object("meta_schedule.PerStoreFeature")
+class PerStoreFeature(FeatureExtractor):
+    """PerStoreFeature extracts one feature vector per BufferStoreNode
+
+    Parameters
+    ----------
+    buffers_per_store : int
+        The number of buffers in each BufferStore; Pad or truncate if necessary.
+    arith_intensity_curve_num_samples : int
+        The number of samples used in the arithmetic intensity curve.
+    cache_line_bytes : int
+        The number of bytes in a cache line.
+    """
+
+    buffers_per_store: int
+    """The number of buffers in each BufferStore; Pad or truncate if necessary."""
+    arith_intensity_curve_num_samples: int  # pylint: disable=invalid-name
+    """The number of samples used in the arithmetic intensity curve."""
+    cache_line_bytes: int
+    """The number of bytes in a cache line."""
+    feature_vector_length: int
+    """Length of the feature vector."""
+
+    def __init__(
+        self,
+        buffers_per_store: int = 5,
+        arith_intensity_curve_num_samples: int = 10,
+        cache_line_bytes: int = 64,
+    ):
+        self.__init_handle_by_constructor__(
+            _ffi_api.FeatureExtractorPerStoreFeature,  # type: ignore # pylint: disable=no-member
+            buffers_per_store,
+            arith_intensity_curve_num_samples,
+            cache_line_bytes,
+        )