[GitHub] [incubator-tvm] masahi commented on a change in pull request #5628: [Relay, Topi][OP] Correlation
masahi commented on a change in pull request #5628:
URL: https://github.com/apache/incubator-tvm/pull/5628#discussion_r428994577
##
File path: topi/tests/python/test_topi_correlation.py
##
@@ -0,0 +1,93 @@
+# 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
+"""test of correlation operator in NCHW layout"""
+import numpy as np
+import tvm
+from tvm import te
+from tvm import autotvm
+import topi
+import topi.testing
+from tvm.contrib.pickle_memoize import memoize
+from topi.util import get_const_tuple
+
+from common import get_all_backend
+
+
+_correlation_implement = {
+"generic": (topi.nn.correlation_nchw,
topi.generic.schedule_correlation_nchw),
+"cuda": (topi.cuda.correlation_nchw, topi.cuda.schedule_correlation_nchw),
+}
+
+
+def verify_correlation_nchw(data_shape, kernel_size, max_displacement,
stride1, stride2, pad_size,
+is_multiply):
+print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d, %d, %d)" %
(data_shape[0], data_shape[1], data_shape[2], data_shape[3],
+ kernel_size,
max_displacement, stride1, stride2, pad_size,
+ is_multiply))
+
+A = te.placeholder(data_shape, name='data1')
+B = te.placeholder(data_shape, name='data2')
+dtype = A.dtype
+
+@memoize("topi.tests.test_topi_correlation_nchw.verify_correlation_nchw")
+def get_ref_data():
+a_np = np.random.uniform(size=data_shape).astype(dtype)
+b_np = np.random.uniform(size=data_shape).astype(dtype)
+c_np = topi.testing.correlation_nchw_python(a_np, b_np, kernel_size,
max_displacement, stride1, stride2, pad_size, is_multiply)
+return a_np, b_np, c_np
+
+a_np, b_np, c_np = get_ref_data()
+
+def check_device(device):
+ctx = tvm.context(device, 0)
+if not ctx.exist:
+print("Skip because %s is not enabled" % device)
+return
+print("Running on target: %s" % device)
+fcompute, fschedule = topi.testing.dispatch(
+device, _correlation_implement)
+with tvm.target.create(device):
+C = fcompute(A, B, kernel_size, max_displacement, stride1,
stride2, pad_size, is_multiply)
+s = fschedule([C])
+
+a = tvm.nd.array(a_np, ctx)
+b = tvm.nd.array(b_np, ctx)
+c = tvm.nd.empty(c_np.shape, dtype=dtype, ctx=ctx)
+
+func = tvm.build(s, [A, B, C], device)
+func(a, b, c)
+tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
+
+for device in ['llvm']:
Review comment:
test on cuda?
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[GitHub] [incubator-tvm] masahi commented on a change in pull request #5628: [Relay, Topi][OP] Correlation
masahi commented on a change in pull request #5628:
URL: https://github.com/apache/incubator-tvm/pull/5628#discussion_r428396485
##
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):
Review comment:
remove direct
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[GitHub] [incubator-tvm] masahi commented on a change in pull request #5628: [Relay, Topi][OP] Correlation
masahi commented on a change in pull request #5628:
URL: https://github.com/apache/incubator-tvm/pull/5628#discussion_r428395419
##
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,
[GitHub] [incubator-tvm] masahi commented on a change in pull request #5628: [Relay, Topi][OP] Correlation
masahi commented on a change in pull request #5628:
URL: https://github.com/apache/incubator-tvm/pull/5628#discussion_r428395349
##
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,
[GitHub] [incubator-tvm] masahi commented on a change in pull request #5628: [Relay, Topi][OP] Correlation
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,
