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? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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