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From GitBox <...@apache.org>
Subject [GitHub] [incubator-tvm] masahi commented on a change in pull request #5628: [Relay, Topi][OP] Correlation
Date Thu, 21 May 2020 01:22:55 GMT

masahi commented on a change in pull request #5628:
URL: https://github.com/apache/incubator-tvm/pull/5628#discussion_r428395061



##########
File path: topi/python/topi/cuda/correlation.py
##########
@@ -0,0 +1,176 @@
+# 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.
+"""Correlation operators on CUDA"""
+import tvm
+from tvm import te
+from tvm import autotvm
+
+from .. import nn
+from ..util import traverse_inline
+
+
+@autotvm.register_topi_compute("correlation_nchw.cuda")
+def correlation_nchw(cfg, data1, data2, kernel_size, max_displacement, stride1, stride2,
padding,
+                     is_multiply):
+    """Correlation operator in NCHW layout.
+
+    Parameters
+    ----------
+    data1 : tvm.te.Tensor
+        4-D with shape [batch, channel, height, width]
+
+    data2 : tvm.te.Tensor
+        4-D with shape [batch, channel, height, width]
+
+    kernel_size: int
+        Kernel size for correlation, must be an odd number
+
+    max_displacement: int
+        Max displacement of Correlation
+
+    stride1: int
+        Stride for data1
+
+    stride2: int
+        Stride for data2 within the neightborhood centered around data1
+
+    padding : int or a list/tuple of 2 or 4 ints
+        Padding size, or
+        [pad_height, pad_width] for 2 ints, or
+        [pad_top, pad_left, pad_bottom, pad_right] for 4 ints
+
+    is_multiply: bocorrelation
+        operation type is either multiplication or substraction
+
+    Returns
+    -------
+    Output : tvm.te.Tensor
+        4-D with shape [batch, out_channel, out_height, out_width]
+    """
+    # pylint: disable=unused-argument
+    return nn.correlation_nchw(data1, data2, kernel_size, max_displacement, stride1, stride2,
+                               padding, is_multiply)
+
+
+def _schedule_direct_correlation_nchw(cfg, s, correlation):
+    """Schedule correlation_nchw direct template"""
+    # pylint: disable=invalid-name
+    ##### space definition begin #####
+    n, f, y, x = s[correlation].op.axis
+    rc, ry, rx = s[correlation].op.reduce_axis
+    cfg.define_split("tile_f", f, num_outputs=4)
+    cfg.define_split("tile_y", y, num_outputs=4)
+    cfg.define_split("tile_x", x, num_outputs=4)
+    cfg.define_split("tile_rc", rc, num_outputs=2)
+    cfg.define_split("tile_ry", ry, num_outputs=2)
+    cfg.define_split("tile_rx", rx, num_outputs=2)
+    cfg.define_knob("auto_unroll_max_step", [0, 512, 1500])
+
+    target = tvm.target.Target.current()
+    if target.target_name in ['nvptx', 'rocm']:
+        cfg.define_knob("unroll_explicit", [1])
+    else:
+        cfg.define_knob("unroll_explicit", [0, 1])
+
+    ##### space definition end #####
+
+    padded_data1, padded_data2 = s[correlation].op.input_tensors
+    s[padded_data1].compute_inline()
+    s[padded_data2].compute_inline()
+
+    # create cache stage
+    s[correlation].set_scope('local')
+    AA = s.cache_read(padded_data1, 'shared', [correlation])
+    BB = s.cache_read(padded_data2, 'shared', [correlation])
+
+    output = s.outputs[0].output(0)
+
+    # tile and bind spatial axes
+    n, f, y, x = s[output].op.axis
+    kernel_scope, n = s[output].split(n, nparts=1)
+
+    bf, vf, tf, fi = cfg["tile_f"].apply(s, output, f)
+    by, vy, ty, yi = cfg["tile_y"].apply(s, output, y)
+    bx, vx, tx, xi = cfg["tile_x"].apply(s, output, x)
+
+    bf = s[output].fuse(n, bf)
+    s[output].bind(bf, te.thread_axis("blockIdx.z"))
+    s[output].bind(by, te.thread_axis("blockIdx.y"))
+    s[output].bind(bx, te.thread_axis("blockIdx.x"))
+    s[output].bind(vf, te.thread_axis("vthread"))
+    s[output].bind(vy, te.thread_axis("vthread"))
+    s[output].bind(vx, te.thread_axis("vthread"))
+    s[output].bind(tf, te.thread_axis("threadIdx.z"))
+    s[output].bind(ty, te.thread_axis("threadIdx.y"))
+    s[output].bind(tx, te.thread_axis("threadIdx.x"))
+    s[output].reorder(bf, by, bx, vf, vy, vx, tf, ty, tx, fi, yi, xi)
+    s[correlation].compute_at(s[output], tx)
+
+    # tile reduction axes
+    n, f, y, x = s[correlation].op.axis
+    rc, ry, rx = s[correlation].op.reduce_axis
+    rco, rci = cfg['tile_rc'].apply(s, correlation, rc)
+    ryo, ryi = cfg['tile_ry'].apply(s, correlation, ry)
+    rxo, rxi = cfg['tile_rx'].apply(s, correlation, rx)
+    s[correlation].reorder(rco, ryo, rxo, rci, ryi, rxi, n, f, y, x)
+
+    s[AA].compute_at(s[correlation], rxo)
+    s[BB].compute_at(s[correlation], rxo)
+
+    # cooperative fetching
+    for load in [AA, BB]:
+        n, f, y, x = s[load].op.axis
+        fused = s[load].fuse(n, f, y, x)
+        tz, fused = s[load].split(fused, nparts=cfg["tile_f"].size[2])
+        ty, fused = s[load].split(fused, nparts=cfg["tile_y"].size[2])
+        tx, fused = s[load].split(fused, nparts=cfg["tile_x"].size[2])
+        s[load].bind(tz, te.thread_axis("threadIdx.z"))
+        s[load].bind(ty, te.thread_axis("threadIdx.y"))
+        s[load].bind(tx, te.thread_axis("threadIdx.x"))
+
+    # unrcorrelationl

Review comment:
       is this typo?




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