[PERF] Parallelize reduction for CPU (#4158)

* [PERF] parallel reduction in cpu

* fix

* x

* update

* lint

* fix

topi/python/topi/x86/__init__.py

@@ -25,6 +25,7 @@
 from .nn import *
 from .conv2d_int8 import *
 from .injective import *
+from .reduction import *
 from .pooling import schedule_pool, schedule_adaptive_pool
 from .bitserial_conv2d import schedule_bitserial_conv2d
 from .bitserial_dense import schedule_bitserial_dense

topi/python/topi/x86/reduction.py

@@ -0,0 +1,120 @@
+# 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=invalid-name
+"""x86 declaration and schedules."""
+from __future__ import absolute_import as _abs
+import tvm
+from .. import tag
+from .. import generic
+from ..util import get_const_tuple
+
+def _schedule_reduce(sch, op, is_idx_reduce=False):
+    if is_idx_reduce:
+        real_out = op.output(0)
+        fused = sch[real_out].fuse(*sch[real_out].op.axis)
+        out = op.input_tensors[0]
+    else:
+        out = op.output(0)
+
+    const_shape = True
+    out_shape = get_const_tuple(out.shape)
+    for d in out_shape:
+        if not isinstance(d, int):
+            const_shape = False
+            break
+
+    if const_shape:
+        naxes = len(sch[out].op.axis)
+        parallelism = 1
+        fuse_axes = []
+        # We choose a heuristic number 128 to limit the maximum parallelism
+        while len(fuse_axes) < naxes and parallelism < 128:
+            ivar = sch[out].op.axis[len(fuse_axes)]
+            parallelism *= int(ivar.dom.extent)
+            fuse_axes.append(ivar)
+        fused = sch[out].fuse(*fuse_axes)
+        sch[out].parallel(fused)
+    else:
+        if len(sch[out].op.axis) >= 5:
+            # avoid too many parallelism
+            fused = sch[out].fuse(sch[out].op.axis[0], sch[out].op.axis[1], sch[out].op.axis[2])
+            sch[out].parallel(fused)
+        else:
+            fused = sch[out].fuse(*sch[out].op.axis)
+            sch[out].parallel(fused)
+
+
+@generic.schedule_reduce.register(["cpu"])
+def schedule_reduce(outs):
+    """X86 schedule for reduction op.
+
+    Parameters
+    ----------
+    outs: Array of Tensor
+          The computation graph description of injective in the format
+          of an array of tensors.
+
+    Returns
+    -------
+    sch: Schedule
+        The computation schedule for the op.
+    """
+    outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
+    sch = tvm.create_schedule([x.op for x in outs])
+    scheduled_ops = []
+
+    def traverse_before_reduce(operator):
+        """Internal travserse function"""
+        if isinstance(operator, tvm.tensor.PlaceholderOp):
+            return
+        if tag.is_injective(operator.tag):
+            sch[operator].compute_inline()
+            for tensor in operator.input_tensors:
+                if tensor.op not in scheduled_ops:
+                    traverse_before_reduce(tensor.op)
+        else:
+            raise RuntimeError("Unsupported operator: %s" % operator.tag)
+
+        scheduled_ops.append(operator)
+
+    def traverse_after_reduce(operator):
+        """Internal travserse function"""
+        if tag.is_broadcast(operator.tag):
+            if operator not in scheduled_ops:
+                generic.schedule_injective_from_existing(sch, operator)
+            for tensor in operator.input_tensors:
+                traverse_after_reduce(tensor.op)
+        elif operator.tag == 'comm_reduce':
+            _schedule_reduce(sch, operator, is_idx_reduce=False)
+            for tensor in operator.input_tensors:
+                if tensor.op not in scheduled_ops:
+                    traverse_before_reduce(tensor.op)
+        elif operator.tag == 'comm_reduce_idx':
+            _schedule_reduce(sch, operator, is_idx_reduce=True)
+            input_tensors = operator.input_tensors[0].op.input_tensors
+            for tensor in input_tensors:
+                if tensor.op not in scheduled_ops:
+                    traverse_before_reduce(tensor.op)
+        elif isinstance(operator, tvm.tensor.PlaceholderOp):
+            pass
+        else:
+            raise RuntimeError("Unsupported operator: %s (tag: %s)" % (operator, operator.tag))
+
+        scheduled_ops.append(operator)
+
+    traverse_after_reduce(outs[0].op)
+    return sch